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1
Wang, Jie. "Incorporating survey weights into logistic regression models." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/267.
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Incorporating survey weights into likelihood-based analysis is a controversial issue because the sampling weights are not simply equal to the reciprocal of selection probabilities but they are adjusted for various characteristics such as age, race, etc. Some adjustments are based on nonresponses as well. This adjustment is accomplished using a combination of probability calculations. When we build a logistic regression model to predict categorical outcomes with survey data, the sampling weights should be considered if the sampling design does not give each individual an equal chance of being selected in the sample. We rescale these weights to sum to an equivalent sample size because the variance is too small with the original weights. These new weights are called the adjusted weights. The old method is to apply quasi-likelihood maximization to make estimation with the adjusted weights. We develop a new method based on the correct likelihood for logistic regression to include the adjusted weights. In the new method, the adjusted weights are further used to adjust for both covariates and intercepts. We explore the differences and similarities between the quasi-likelihood and the correct likelihood methods. We use both binary logistic regression model and multinomial logistic regression model to estimate parameters and apply the methods to body mass index data from the Third National Health and Nutrition Examination Survey. The results show some similarities and differences between the old and new methods in parameter estimates, standard errors and statistical p-values.
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Konis, Kjell Peter. "Linear programming algorithms for detecting separated data in binary logistic regression models." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a.
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This thesis is a study of the detection of separation among the sample points in binary logistic regression models. We propose a new algorithm for detecting separation and demonstrate empirically that it can be computed fast enough to be used routinely as part of the fitting process for logistic regression models. The parameter estimates of a binary logistic regression model fit using the method of maximum likelihood sometimes do not converge to finite values. This phenomenon (also known as monotone likelihood or infinite parameters) occurs because of a condition among the sample points known as separation. There are two classes of separation. When complete separation is present among the sample points, iterative procedures for maximizing the likelihood tend to break down, when it would be clear that there is a problem with the model. However, when quasicomplete separation is present among the sample points, the iterative procedures for maximizing the likelihood tend to satisfy their convergence criterion before revealing any indication of separation. The new algorithm is based on a linear program with a nonnegative objective function that has a positive optimal value when separation is present among the sample points. We compare several approaches for solving this linear program and find that a method based on determining the feasibility of the dual to this linear program provides a numerically reliable test for separation among the sample points. A simulation study shows that this test can be computed in a similar amount of time as fitting the binary logistic regression model using the method of iteratively reweighted least squares: hence the test is fast enough to be used routinely as part of the fitting procedure. An implementation of our algorithm (as well as the other methods described in this thesis) is available in the R package safeBinaryRegression.
3
Zhang, Dongquan. "Effects of model selection on the coverage probability of confidence intervals in binary-response logistic regression." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8538.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Measurement, Statistics and Evaluation. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
4
Bergtold, Jason Scott. "Advances in Applied Econometrics: Binary Discrete Choice Models, Artificial Neural Networks, and Asymmetries in the FAST Multistage Demand System." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27266.
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The dissertation examines advancements in the methods and techniques used in the field of econometrics. These advancements include: (i) a re-examination of the underlying statistical foundations of statistical models with binary dependent variables. (ii) using feed-forward backpropagation artificial neural networks for modeling dichotomous choice processes, and (iii) the estimation of unconditional demand elasticities using the flexible multistage demand system with asymmetric partitions and fixed effects across time.
The first paper re-examines the underlying statistical foundations of statistical models with binary dependent variables using the probabilistic reduction approach. This re-examination leads to the development of the Bernoulli Regression Model, a family of statistical models arising from conditional Bernoulli distributions. The paper provides guidelines for specifying and estimating a Bernoulli Regression Model, as well as, methods for generating and simulating conditional binary choice processes. Finally, the Multinomial Regression Model is presented as a direct extension.
The second paper empirically compares the out-of-sample predictive capabilities of artificial neural networks to binary logit and probit models. To facilitate this comparison, the statistical foundations of dichotomous choice models and feed-forward backpropagation artificial neural networks (FFBANNs) are re-evaluated. Using contingent valuation survey data, the paper shows that FFBANNs provide an alternative to the binary logit and probit models with linear index functions. Direct comparisons between the models showed that the FFBANNs performed marginally better than the logit and probit models for a number of within-sample and out-of-sample performance measures, but in the majority of cases these differences were not statistically significant. In addition, guidelines for modeling contingent valuation survey data and techniques for estimating median WTP measures using FFBANNs are examined.
The third paper estimates a set of unconditional price and expenditure elasticities for 49 different processed food categories using scanner data and the flexible and symmetric translog (FAST) multistage demand system. Due to the use of panel data and the presence of heterogeneity across time, temporal fixed effects were incorporated into the model. Overall, estimated price elasticities are larger, in absolute terms, than previous estimates. The use of disaggregated product groupings, scanner data, and the estimation of unconditional elasticities likely accounts for these differences.<br>Ph. D.
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Beebe, Claire Elizabeth. "A comparison of stratified and unstratified modeling for binary logistic regression in the presence of a simulated interaction." Oklahoma City : [s.n.], 2008.
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6
Lopez, Andrea Salome Viteri. "Caracterização da chuva estimada pelo radar durante eventos de alagamento na cidade de São Paulo." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/14/14133/tde-25092018-163917/.
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Este projeto de mestrado apresenta uma caracterização das chuvas estimadas pelo radar meteorológico Doppler de dupla polarização banda S (SPOL) do Departamento de Águas e Energia Elétrica (DAEE) e Fundação Centro Tecnológico de Hidráulica (FCTH) durante eventos com ou sem alagamento para cada bairro da cidade de São Paulo durante o ano de 2015. A caracterização foi determinada a partir da função densidade de probabilidade (PDF) da chuva acumulada e da taxa de precipitação, duração da chuva e fração da área de cada bairro onde ocorreu a chuva. Na média, os eventos de alagamento estavam associados com um volume de chuva maior que 30mm e taxa precipitação máxima maior que 30mm/h. Com relação à duração não foi possível encontrar um padrão médio, pois a chuva teve duração mínima de 20 minutos e máxima de 23 horas. Por outro lado, eventos de alagamento tinham alcançado mais de 27% da área do bairro com taxa de precipitação maior que 30 mm/h e 50 mm/h. Destaca-se ao longo desta análise que os bairros localizados próximos aos rios Tietê e Pinheiros e a região central da cidade de São Paulo apresentaram maior probabilidade de ocorrência de alagamento com volumes de chuva mais baixos do que a média de 30 mm por dia e também registraram maior recorrência de pontos alagados. Por último foi desenvolvido um método de regressão logística binária para calcular a probabilidade de ocorrência de alagamentos nos diversos bairros da cidade São Paulo. Este modelo utiliza como parâmetros de entrada a duração da chuva, a taxa de precipitação máxima e a chuva acumulada nas últimas 24 horas. O modelo apresentou uma probabilidade de detecção (POD) média de 1% e uma taxa de falso alarme média (FAR) de 0,6 para os eventos de alagamento, já para eventos sem alagamento o POD médio foi de 96% e a FAR foi de 2,5%. Portanto o modelo consegue prever os casos sem alagamento.<br>This dissertation project presents a characterization of the rainfall estimated from a dual-polarization S-band Doppler meteorological radar (SPOL) of the Department of Water and Electric Energy (DAEE) and Foundation Technological Center of Hydraulics (FCTH) during with or without flooding events for each neighborhood of the city of São Paulo over the year 2015. The characterization was determined by the probability density function (PDF) of the accumulated rainfall and the precipitation rate, rainfall duration and rainfall-area fraction in the neighborhoods. In average, flood events were associated with a rainfall volume greater than 30mm and a maximum rainfall rate greater than 30mm/h. Regarding the duration, it was not possible to find an average pattern, because the rain had a minimum duration of 20 minutes and a maximum of 23 hours. On the other hand, flood events had reached more than 27% of the neighborhood\'s area with a precipitation rate greater than 30 mm/h and 50 mm/h. It is highlighted throughout this analysis that the neighborhoods located near the Tietê and Pinheiros rivers and central region of the city of São Paulo presented a higher probability of flood occurrence with rainfall volumes lower than the average of 30 mm per day and also recorded higher recurrence of flooded spots. Finally, a binary logistic regression method was developed to estimate the probability of occurrence of flooding in the various neighborhoods of the city of São Paulo. This model uses as input parameters rainfall duration, maximum rainfall rate and accumulated rainfall in the last 24 hours. The model presented a mean probability of detection (POD) of 1% and a mean false alarm rate (FAR) of 0,6 for flood events. On the other hand, for events without occurrence of flood a mean POD was 96% and FAR 2,5. Therefore, the model can predict the events without flooding.
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Sperry, Rita A. "Prediction of retention and probation status of first-year college students in learning communities using binary logistic regression models." Thesis, Texas A&M University - Corpus Christi, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3626219.
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<p> The first year of college is a critical period of transition for incoming college students. Learning communities have been identified as an approach to link students together in courses that are intentionally integrated and designed with first-year students' needs in mind. Yet, learning community teaching teams are often not provided with data prior to the start of the semester about their students in order to target interventions. Also, it remains unclear as to which students are most benefitted by participating in learning communities. One question then becomes, what variables known on or before the first day of classes are predictive of first-year student success, in terms of retention and probation status, for first-year college students in learning communities? </p><p> The correlational study employed univariate and multivariate analyses on pre-college data about three consecutive cohorts of first-year students in learning communities at a regional public university in South Texas. Logistic regression models were developed to predict retention and probation status without respect to learning community membership, as well as for each learning community category. </p><p> Results indicated that group differences were not statistically significant based on either first-generation status or age for retention, while group differences were statistically significant for probation status on the basis of all of the pre-college variables except age. Although statistically significant differences were found among the learning community categories for each of the pre-college variables, there were no statistically significant group differences in their retention or probation rates. </p><p> The model to predict retention regardless of learning community membership included five variables, while the model to predict probation status included eight variables. The models for each learning community contained different sets of predictor variables; the most common predictors of retention or probation status were high school percentile and orientation date. </p><p> The study has practical implications for admissions officers, orientation planners, student support services, and learning community practitioners. It is recommended to replicate the study with more recent learning community cohorts and additional pre-college variables, as well as in programs across the nation, to contribute to the literature about the potential for learning communities to enhance first-year student success.</p>
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Aslan, Yasemin. "Which Method Gives The Best Forecast For Longitudinal Binary Response Data?: A Simulation Study." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612582/index.pdf.
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Panel data, also known as longitudinal data, are composed of repeated measurements taken from the same subject over different time points. Although it is generally used in time series applications, forecasting can also be used in panel data due to its time dimension. However, there is limited number of studies in this area in the literature. In this thesis, forecasting is studied for panel data with binary response because of its increasing importance and increasing fundamental roles. A simulation study is held to compare the efficiency of different methods and to find the one that gives the optimal forecast values. In this simulation, 21 different methods, including naï<br>ve and complex ones, are used by the help of R software. It is concluded that transition models and random effects models with no lag of response can be chosen for getting the most accurate forecasts, especially for the first two years of forecasting.
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Katta, Vanishravan. "Development of Crash Severity Model for Predicting Risk Factors in Work Zones for Ohio." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384556981.
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Aphane, Mogau Marvin. "Small-scale mango farmers, transaction costs and changing agro-food markets: evidence from Vhembe and Mopani districts, Limpopo Province." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7333_1365584421.
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<p>The main objective of this study was to identify ways in which transaction costs can be lowered to improve small-scale farmers&rsquo<br>participation in and returns from agricultural output markets, with specific reference to small-scale mango farmers in Limpopo province. This study hypothesizes that transaction costs are lower in informal spot markets and increase when small-scale farmers sell in more structured markets (formal markets). This study builds on transaction cost economics (TCE) to demonstrate how to overcome transaction cost barriers that small-scale mango farmers face in the agro-food markets. The approach to collect primary information was sequenced in two steps: first, key informant and focus group interviews were conducted and, secondly, a structured survey instrument was administered in two districts of Limpopo. A total of 235 smallscale mango farmers were interviewed. A binary logistic regression model was used to estimate the impact of transaction costs on the likelihood of households&rsquo<br>participation in formal (=1) and informal (=0) agro-food markets. STATA Version 10 was used to analyse the data. This study found that a larger proportion of male than female farming households reported participation in the formal markets, suggesting deep-seated gender differentiation in market participation. The average age of small farmers participating in formal markets is 52, compared to 44 for those in informal markets, implying that older farmers might have established stronger networks and acquired experience over a longer period. Farmers staying very far from the densely populated towns (more than 50 km) participate less in the formal markets than those staying closer (0 &ndash<br>25 km and 26 &ndash<br>49 km), which implies that the further they are from the towns, the less the likelihood of farmers selling in the formal markets. Farmers who own storage facilities and a bakkie (transportation means) participate more in formal markets compared to those who do not own these assets, which suggests that these farmers are able to store mangoes, retaining their freshness and subsequently delivering them to various agro-food markets on time. Households that participate in formal markets have high mean values of income and social grants. However, this study found that the likelihood of a household&rsquo<br>s participation in the markets is less as income and social grants increase. This suggests that households do not invest their financial assets in order to overcome market access barriers. A large proportion of households that own larger pieces of arable land participate in the formal markets, which implies that they are able to produce marketable surplus. Households that have a high mean value (in Rand) of cattle participate more in formal markets than in informal markets. However, this study found that the likelihood of a household&rsquo<br>s participation in the markets does not change with an increase in the value of its livestock. These findings suggest that households do not sell their cattle in order to overcome market access barriers. Reduced transaction costs for small-scale mango farmers in Limpopo should improve their participation in and returns from the agro-food markets. Policy interventions to support this need to focus on: access to storage and transportation facilities, enforcement of gender equity requirements in existing policies, and better access to information about markets.</p>
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Shrestha, Pramen P., and Joseph Shrestha. "Factors Associated with Crash Severities in Built-up Areas Along Rural Highways of Nevada: A Case Study of 11 Towns." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/714.
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In 2014, 32,675 deaths were recorded in vehicle crashes within the United States. Out of these, 51% of the fatalities occurred in rural highways compared to 49% in urban highways. No specific crash data are available for the built-up areas along rural highways. Due to high fatalities in rural highways, it is important to identify the factors that cause the vehicle crashes. The main objective of this study is to determine the factors associated with severities of crashes that occurred in built-up areas along the rural highways of Nevada. Those factors could aid in making informed decisions while setting up speed zones in these built-up areas. Using descriptive statistics and binary logistic regression model, 337 crashes that occurred in 11 towns along the rural highways from 2002 to 2010 were analyzed. The results showed that more crashes occurred during favorable driving conditions, e.g., 87% crashes on dry roads and 70% crashes in clear weather. The binary logistic regression model showed that crashes occurred from midnight until 4 a.m. were 58.3% likely to be injury crashes rather than property damage only crashes, when other factors were kept at their mean values. Crashes on weekdays were three times more likely to be injury crashes than that occurred on weekends. When other factors were kept at their mean value, crashes involving motorcycles had an 80.2% probability of being injury crashes. Speeding was found to be 17 times more responsible for injury crashes than mechanical defects of the vehicle. As a result of this study, the Nevada Department of Transportation now can take various steps to improve public safety, including steps to reduce speeding and encourage the use of helmets for motorcycle riders.
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Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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MOREIRA, RODRIGO PINTO. "SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13437@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos parâmetros da parte linear passa a ser feita por Máxima Verossimilhança. Assim o modelo, que é paramétrico não-linear e estruturado por árvore de decisão, onde cada nó terminal representa um regime os quais têm seus parâmetros estimados da mesma forma que em uma Regressão Logística, é denominado Smooth Transition Logistic Regression-Tree (STLR-Tree). A inclusão dos regimes, determinada pela divisão dos nós da árvore, é feita baseada em testes do tipo Multiplicadores de Lagrange, que em sua forma para o caso Gaussiano utiliza a Soma dos Quadrados dos Resíduos em suas estatísticas de teste, aqui são substituídas pela Função Desvio (Deviance), que é equivalente para o caso dos modelos não Gaussianos, cuja distribuição da variável dependente pertença à família exponencial. Na aplicação a dados reais selecionou-se dois conjuntos das variáveis explicativas de cada uma das duas bases utilizadas, que resultaram nas melhores taxas de acerto, verificadas através de Tabelas de Classificação (Matrizes de Confusão). Esses conjuntos de variáveis foram usados com outros métodos de classificação existentes, são eles: Generalized Additive Models (GAM), Regressão Logística, Redes Neurais, Análise Discriminante, k-Nearest Neighbor (K-NN) e Classification and Regression Trees (CART).<br>The main goal of this work is to adapt the STR-Tree model, which is the combination of a Smooth Transition with Regression model with Classi cation and Regression Tree (CART), in order to use it in Classification. Some changes were made in its structural form and in the estimation. Due to the fact we are doing binary dependent variables classification, is necessary to use the techniques employed in Logistic Regression, so the estimation of the linear part will be made by Maximum Likelihood. Thus the model, which is nonlinear parametric and structured by a decision tree, where each terminal node represents a regime that have their parameters estimated in the same way as in a Logistic Regression, is called Smooth Transition Logistic Regression Tree (STLR-Tree). The inclusion of the regimes, determined by the splitting of the tree's nodes, is based on Lagrange Multipliers tests, which for the Gaussian cases uses the Residual Sum-of-squares in their test statistic, here are replaced by the Deviance function, which is equivalent to the case of non-Gaussian models, that has the distribution of the dependent variable in the exponential family. After applying the model in two datasets chosen from the bibliography comparing with other methods of classi cation such as: Generalized Additive Models (GAM), Logistic Regression, Neural Networks, Discriminant Analyses, k-Nearest Neighbor (k-NN) and Classification and Regression Trees (CART). It can be seen, verifying in the Classification Tables (Confusion Matrices) that STLR-Tree showed the second best result for the overall rate of correct classification in three of the four applications shown, being in all of them, behind only from GAM.
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Jun, Shi. "Frequentist Model Averaging For Functional Logistic Regression Model." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352519.
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Frequentist model averaging as a newly emerging approach provides us a way to overcome the uncertainty caused by traditional model selection in estimation. It acknowledges the contribution of multiple models, instead of making inference and prediction purely based on one single model. Functional logistic regression is also a burgeoning method in studying the relationship between functional covariates and a binary response. In this paper, the frequentist model averaging approach is applied to the functional logistic regression model. A simulation study is implemented to compare its performance with model selection. The analysis shows that when conditional probability is taken as the focus parameter, model averaging is superior to model selection based on BIC. When the focus parameter is the intercept and slopes, model selection performs better.
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Abel, Leah A. "Development and maintenance of victimization associated with bullying during the transition to middle school: The role of school-based factors." Kent State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=kent1594745288709797.
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Liu, Ying. "On goodness-of-fit of logistic regression model." Diss., Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/530.
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Heise, Mark A. "Optimal designs for a bivariate logistic regression model." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38538.
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In drug-testing experiments the primary responses of interest are efficacy and toxicity. These can be modeled as a bivariate quantal response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In addition, a new optimality criterion, T -optimality, is developed which minimizes the asymptotic variance of the estimate of the therapeutic index.
Most experimenters will be less familiar with the Gumbel bivariate logistic regression model than with the univariate logistic regression models which comprise its marginals. Therefore, the optimal designs based on the Gumbel model are evaluated based on univariate logistic regression D-efficiencies; conversely, designs derived from the univariate logistic regression model are evaluated with respect to the Gumbel optimality criteria.
Further practical considerations motivate an exploration of designs providing a maximum compromise between the three Gumbel-based criteria D, Q and T. Finally, 5-point designs which can be generated by fitted equations are proposed as a practical option for experimental use.<br>Ph. D.
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Rodrigues, José Tenylson Gonçalves. "Análise de dados longitudinais para variáveis binárias." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4531.
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Previous issue date: 2009-03-05<br>Financiadora de Estudos e Projetos<br>The objective of this work is to present techniques of regression analysis for longitudinal data when the response variable is binary. Initially, there is a review of generalized linear models, marginal models, transition models, mixed models, and logistic regression methods of estimation, which will be necessary for the development of work. In addition to the methods of estimation, some structures of correlation will be studied in an attempt to capture the intra-individual serial dependence over time. These methods were applied in two situations, one where the response variable is continuous and normal distribution, and another when the response variable has the Bernoulli distribution. It was also sought to explore and present techniques for selection of models and diagnostics for the two cases. Finally, an application of the above methodology will be presented using a set of real data.<br>O objetivo deste trabalho é apresentar técnicas de análise de regressão para dados longitudinais quando a variável resposta é binária. Inicialmente, é feita uma revisão sobre modelos lineares generalizados, modelos marginais, modelos de transição, modelos mistos, regressão logística e métodos de estimação, pois serão necessários para o desenvolvimento do trabalho. Além dos métodos de estimação, algumas estruturas de correlação serão estudadas, na tentativa de captar a dependência serial intra-indivíduo ao longo do tempo. Estes métodos foram aplicados em duas situações; uma quando a variável resposta é contínua, e se assume ter distribuição normal, e a outra quando a variável resposta assume ter distribuição de Bernoulli. Também se procurou pesquisar e apresentar técnicas de seleção de modelos e de diagnósticos para os dois casos. Ao final, uma aplicação com a metodologia pesquisada será apresentada utilizando um conjunto de dados reais.
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Pan, Tianshu. "Using the multivariate multilevel logistic regression model to detect DIF a comparison with HGLM and logistic regression DIF detection methods /." Diss., Connect to online resource - MSU authorized users, 2008.
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Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008.<br>Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.
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KARAM, KARINE DE ALMEIDA. "LOGISTIC REGRESSION: A MODEL TO MEASURE SIGNATURE´S CANCELLATION RISK." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8259@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O tema central deste projeto é a retenção de clientes como
estratégia
competitiva para aumentar a lucratividade da empresa. O
objetivo é desenvolver
um modelo estatístico que relacione variáveis
transacionais, demográficas e dados
sobre o histórico de eventos com a probabilidade de
cancelamento dos clientes
assinantes de jornal e definir o perfil dos clientes com
maior risco de
desligamento. Em uma primeira etapa, este estudo fornece
uma revisão teórica
sobre lealdade, satisfação e marketing de relacionamento,
a fim de buscar uma
relação com a retenção de clientes. Em seguida, a revisão
de literatura levantou as
variáveis mais usadas na segmentação de clientes tais
como: variáveis
transacionais, geográficas, demográficas, psicográficas e
comportamentais para
definir o perfil dos clientes que cancelam e dos que não
cancelam sua assinatura.
Depois de construir um modelo teórico, a regressão
logística foi utilizada como
técnica estatística para desenvolver um modelo de previsão
de cancelamento. Os
resultados foram analisados com o auxílio do programa
estatístico SPSS e
conclui-se que o perfil do cliente que cancela a
assinatura do jornal é o jovem de
até 30 anos; com baixo nível sócio-demográfico; morador da
baixada, subúrbio e
outros estados que não o Rio de Janeiro; que tenha
adquirido sua assinatura
através do canal telemarketing ativo; com a assinatura da
modalidade anual e
forma de pagamento em boleto ou débito em conta corrente;
clientes que
adquiriram sua assinatura mais recentemente; que comprem
menos de 3 produtos
da empresa e que não tenham feito reclamações através da
central de atendimento.
O modelo final de previsão de cancelamento contou com 11
variáveis e a tabela
de classificação mostrou uma taxa de acerto geral de 75,3%.
A última etapa apresenta algumas conclusões, implicações e
sugestões para
pesquisas futuras.<br>The core subject of this project is the customers´
retention as a competitive
strategy to increase the company´s profitability. The goal
is to develop a statistical
model that links transactional and demographic variables
and customer´s history
data with the subscribers´ churn of a certain publication.
In the first part, this
study provides a revision on loyalty, satisfaction and
relationship marketing
theory in order to find a relation with customers´
retention. After that, the
literature revision raised the most used variables for the
segmentation of
customers, such as: transactional, geographic,
demographic, psycological and
behavior variables to define the profile of the customer
who churns and the profile
of that one who doesn´t. After constructing a theoretical
model, the logistic
regression was used as a statistical technique to develop
a model of cancellation
forecasting. The results has been analyzed with the aid of
statistical program SPSS
and conclude that the profile of the customer who cancels
the subscription of the
publication is young up to 30 years old; with low social-
demographic level; living
at Baixada, Suburb, and other states than Rio De Janeiro;
that bought the
subscription through the outbound telemarketing sales
channel; with one year
subscription and payment through invoice or direct debit
in current account;
customers who has bought its signature more recently; that
do not buy less than 3
other products of the company and that have not made
complaints through the
customer service. The final model of churn forecasting
uses 11 variables and the
classification table showed an accuracy of 75,3%. The last
part presents some
conclusions, implications and suggestions for future
research.
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Zimmer, Zachary. "Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.
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The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.
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Signorini, David F. "Practical aspects of kernel smoothing for binary regression and density estimation." Thesis, n.p, 1998. http://oro.open.ac.uk/19923/.
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Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.
Full textAbstract:
The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix against not helix (H/∼H), sheet against not sheet (S/∼S) and coil against not coil (C/∼C). The performance of these binary classifiers are compared using the overall accuracy in predicting the protein secondary structure for various window sizes. Our result indicate that hold-out cross validation achieved higher accuracy than 5-fold cross validation. The Naive Bayes classifier, using 5-fold cross validation achieved, the lowest accuracy for predicting helix against not helix. The classification tree classifiers, using 5-fold cross validation, achieved the lowest accuracies for both coil against not coil and sheet against not sheet classifications. The logistic regression classier accuracy is dependent on the window size; there is a positive relationship between the accuracy and window size. The logistic regression classier approach achieved the highest accuracy when compared to the classification tree and Naive Bayes classifiers for each classification task; predicting helix against not helix with accuracy 77.74 percent, for sheet against not sheet with accuracy 81.22 percent and for coil against not coil with accuracy 73.39 percent. It is noted that it is easier to compare classifiers if the classification process could be completely facilitated in R. Alternatively, it would be easier to assess these logistic regression classifiers if SPSS had a function to determine the accuracy of the logistic regression classifier.
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Letsinger, William C. "Optimal one and two-stage designs for the logistic regression model /." This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02132009-171953/.
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Letsinger, William C. II. "Optimal one and two-stage designs for the logistic regression model." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/37354.
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Binary response data is often modeled using the logistic regression model, a well known nonlinear model. Designing an optimal experiment for this nonlinear situation poses some problems not encountered with a linear model. The application of several optimality design criteria to the logistic regression model is explored, and many resulting optimal designs are given. The implementation of these optimal designs requires the parameters of the model to be known. However, the model parameters are not known. If they were, there would be no need to design an experiment. Consequently the parameters must be estimated prior to implementing a design.
Standard one-stage optimal designs are quite sensitive to parameter misspecification and are therefore unsatisfactory in practice. A two-stage Bayesian design procedure is developed which effectively deals with poor parameter knowledge while maintaining high efficiency. The first stage makes use of Bayesian design as well as Bayesian estimation in order to cope with parameter misspecification. Using the parameter estimates from the first stage, the second stage conditionally optimizes a chosen design optimality criterion. Asymptotically, the two-stage design procedure is considerably more efficient than the one-stage design when the parameters are misspecified and only slightly less efficient when the parameters are known. The superiority of the two-stage procedure over the one-stage is even more evident for small samples.<br>Ph. D.
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Liu, Xiang. "A Multi-Indexed Logistic Model for Time Series." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3140.
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In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare their performance using forecast accuracy and an area under the curve score via simulated sine waves with various intensities of Gaussian noise and Standard & Poors 500 historical data. Overall, that MILR outperforms SLR is validated on both realistic and simulated data. Finally, some possible future directions of research are discussed.
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Strandberg, Rickard, and Johan Låås. "A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252556.
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The primary purpose of this thesis is to predict whether or not a customer will make a purchase from a specific item category. The historical data is provided by the Nordic online-based IT-retailer Dustin. The secondary purpose is to evaluate how well a fully connected feed forward neural network performs as compared to Lasso regularized logistic regression and gradient boosted trees (XGBoost) on this task. This thesis finds XGBoost to be superior to the two other methods in terms of prediction accuracy, as well as speed.<br>Det primära syftet med denna uppsats är att förutsäga huruvida en kund kommer köpa en specifik produkt eller ej. Den historiska datan tillhandahålls av den Nordiska internet-baserade IT-försäljaren Dustin. Det sekundära syftet med uppsatsen är att evaluera hur väl ett djupt neuralt nätverk presterar jämfört med Lasso regulariserad logistisk regression och gradient boostade träd (GXBoost). Denna uppsats fann att XGBoost presterade bättre än de två andra metoderna i såväl träffsäkerhet, som i hastighet.
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Rusch, Thomas, Ilro Lee, Kurt Hornik, Wolfgang Jank, and Achim Zeileis. "Influencing Elections with Statistics: Targeting Voters with Logistic Regression Trees." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3458/1/Report117.pdf.
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Political campaigning has become a multi-million dollar business. A substantial proportion of a campaign's budget is spent on voter mobilization, i.e., on identifying and
influencing as many people as possible to vote. Based on data, campaigns use statistical
tools to provide a basis for deciding who to target. While the data available is usually rich,
campaigns have traditionally relied on a rather limited selection of information, often including only previous voting behavior and one or two demographical variables. Statistical
procedures that are currently in use include logistic regression or standard classification
tree methods like CHAID, but there is a growing interest in employing modern data mining approaches. Along the lines of this development, we propose a modern framework
for voter targeting called LORET (for logistic regression trees) that employs trees (with
possibly just a single root node) containing logistic regressions (with possibly just an intercept) in every leaf. Thus, they contain logistic regression and classification trees as special
cases and allow for a synthesis of both techniques under one umbrella. We explore various
flavors of LORET models that (a) compare the effect of using the full set of available
variables against using only limited information and (b) investigate their varying effects
either as regressors in the logistic model components or as partitioning variables in the
tree components. To assess model performance and illustrate targeting, we apply LORET
to a data set of 19,634 eligible voters from the 2004 US presidential election. We find that
augmenting the standard set of variables (such as age and voting history) together with
additional predictor variables (such as the household composition in terms of party affiliation and each individual's rank in the household) clearly improves predictive accuracy.
We also find that LORET models based on tree induction outbeat the unpartitioned competitors. Additionally, LORET models using both partitioning variables and regressors
in the resulting nodes can improve the efficiency of allocating campaign resources while
still providing intelligible models.<br>Series: Research Report Series / Department of Statistics and Mathematics
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Venkataraman, Aarti. "Comparison of neural network and logistic regression model to predict meical outcome." Cincinnati, Ohio : University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1097000476.
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Fu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.
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The analysis of big data is of great interest today, and this comes with challenges of improving precision and efficiency in estimation and prediction. We study binary data with covariates from numerous small areas, where direct estimation is not reliable, and there is a need to borrow strength from the ensemble. This is generally done using Bayesian logistic regression, but because there are numerous small areas, the exact computation for the logistic regression model becomes challenging. Therefore, we develop an integrated multivariate normal approximation (IMNA) method for binary data with covariates within the Bayesian paradigm, and this procedure is assisted by the empirical logistic transform. Our main goal is to provide the theory of IMNA and to show that it is many times faster than the exact logistic regression method with almost the same accuracy. We apply the IMNA method to the health status binary data (excellent health or otherwise) from the Nepal Living Standards Survey with more than 60,000 households (small areas). We estimate the proportion of Nepalese in excellent health condition for each household. For these data IMNA gives estimates of the household proportions as precise as those from the logistic regression model and it is more than fifty times faster (20 seconds versus 1,066 seconds), and clearly this gain is transferable to bigger data problems.
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Alkhalaf, Arwa A. "The impact of predictor variable(s) with skewed cell probabilities on the Wald test in binary logistic regression." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61232.
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What happens to the parameter estimates and test operating characteristics when the predictor variables in a logistic regression are skewed? The statistics literature provides relatively few answers to this question. A series of simulation studies are reported that investigated the impact of a skewed predictor (s) on the Type I error rate and power of the Wald test in a logistic regression model. Five simulations were conducted for three different models: a simple logistic regression with a binary predictor, a simple logistic regression with a continuous predictor, and a multiple logistic regression with two dichotomous predictors. The results show that the Type I error rate and power were affected by severe predictor skewness, but that the effect was moderated by sample size. The Type I error rate was consistently deflated for all three models. Also, power improved with less skewness. A detailed description of the impact of skewed cell predictor probabilities and sample size provide guidelines for practitioners as to where to expect the greatest problems. These findings highlight the importance of the effects of predictor characteristics on statistical analysis of a logistic regression.<br>Education, Faculty of<br>Educational and Counselling Psychology, and Special Education (ECPS), Department of<br>Graduate
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Khajuria, Saket. "A Model to Predict Student Matriculation from Admissions Data." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960.
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Hossain, Shahadut. "Dealing with measurement error in covariates with special reference to logistic regression model: a flexible parametric approach." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/408.
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In many fields of statistical application the fundamental task is to quantify the association between some explanatory variables or covariates and a response or outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely we measure the relevant covariates. In many instances, we can not measure some of the covariates accurately, rather we can measure noisy versions of them. In statistical terminology this is known as measurement errors or errors in variables. Regression analyses based on noisy covariate measurements lead to biased and inaccurate inference about the true underlying response-covariate associations.
In this thesis we investigate some aspects of measurement error modelling in the case of binary logistic regression models. We suggest a flexible parametric approach for adjusting the measurement error bias while estimating the response-covariate relationship through logistic regression model. We investigate the performance of the proposed flexible parametric approach in comparison with the other flexible parametric and nonparametric approaches through extensive simulation studies. We also compare the proposed method with the other competitive methods with respect to a real-life data set. Though emphasis is put on the logistic regression model the proposed method is applicable to the other members of the generalized linear models, and other types of non-linear regression models too. Finally, we develop a new computational technique to approximate the large sample bias that my arise due to exposure model misspecification in the estimation of the regression parameters in a measurement error scenario.
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Matshego, James Moeng. "The variable selection problem and the application of the roc curve for binary outcome variables." Diss., Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-08112008-104847.
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MATYATIM, Rosliza. "The Classification Model for Corporate Failures in Malaysia." Graduate School of International Development, Nagoya University, 2006. http://hdl.handle.net/2237/7314.
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Belyaeva, Elena. "On a new logistic regression model for bankruptcy prediction in the IT branch." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242789.
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Chien, Yung-Lin, and 簡詠霖. "Using Conditional Logistic Regression Model in Classifying Binary Data." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/60527189839530107512.
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"Three Essays on Correlated Binary Outcomes: Detection and Appropriate Models." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49005.
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abstract: Correlation is common in many types of data, including those collected through longitudinal studies or in a hierarchical structure. In the case of clustering, or repeated measurements, there is inherent correlation between observations within the same group, or between observations obtained on the same subject. Longitudinal studies also introduce association between the covariates and the outcomes across time. When multiple outcomes are of interest, association may exist between the various models. These correlations can lead to issues in model fitting and inference if not properly accounted for. This dissertation presents three papers discussing appropriate methods to properly consider different types of association. The first paper introduces an ANOVA based measure of intraclass correlation for three level hierarchical data with binary outcomes, and corresponding properties. This measure is useful for evaluating when the correlation due to clustering warrants a more complex model. This measure is used to investigate AIDS knowledge in a clustered study conducted in Bangladesh. The second paper develops the Partitioned generalized method of moments (Partitioned GMM) model for longitudinal studies. This model utilizes valid moment conditions to separately estimate the varying effects of each time-dependent covariate on the outcome over time using multiple coefficients. The model is fit to data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to investigate risk factors of childhood obesity. In the third paper, the Partitioned GMM model is extended to jointly estimate regression models for multiple outcomes of interest. Thus, this approach takes into account both the correlation between the multivariate outcomes, as well as the correlation due to time-dependency in longitudinal studies. The model utilizes an expanded weight matrix and objective function composed of valid moment conditions to simultaneously estimate optimal regression coefficients. This approach is applied to Add Health data to simultaneously study drivers of outcomes including smoking, social alcohol usage, and obesity in children.<br>Dissertation/Thesis<br>Doctoral Dissertation Statistics 2018
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Liu, Ying. "A model for multivariate binary data with covariates based on compatible conditionally specified logistic regressions." Thesis, 1994. http://hdl.handle.net/2429/5380.
Full textAbstract:
Rather than construction of a multivariate distribution from given univariate or bivariate
margins, recently several papers seek to promote the development and usage of a
simple but relatively unknown approach to the specification of models for dependent
binary outcomes through conditional probabilities, each of which is assumed to be logistic.
These recent proposals were all offered as heuristic approaches to specifying a
multivariate distribution capable of representing the dependence of binary outcomes.
However, they are limited in scope, for they all describe some special patterns of dependence.
This thesis is concerned with a model for a multivariate binary response with
covariates based on compatible conditionally specified logistic regressions. With this
model, we allow for a general dependence structure for the binary outcomes.
Three likelihood-based computing methods are introduced to estimate the parameters
in our model. An example on the coronary bypass surgery is presented for illustration.
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Chang, Wan-Chi, and 張琬琦. "A Comparison of Classification Methods for Binary Data in Logistic Regression Model, Discriminant Analysis, and CART － An example of Down syndrome." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/76480910410292428934.
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碩士<br>國立陽明大學<br>公共衛生研究所<br>90<br>Statistical classification applications are very popular in clinical medicine. For example, logistic regression is used to analyze dataset which performances two mutually exclusive events, and discriminant analysis is appropriate to classify several classes under some parametric assumptions such as multivariate normal distribution or testing homogeneity of covariate matrices. We attempt to use classification and regression tree（CART） which is nonparametric method to classification. Typically, the accuracy of those classification algorithms are showed to be compared with each other now. In this thesis, we aim to compare the areas of receiver operation characteristic curves （ROC Curves） to assess those methods. Down syndrome is the common chromosomal anomalies disease and its incidence rate in Taiwan is about 1.18/1000. The Taipei Veterans General Hospital has provided 912 singleton pregnancies contains 45 Down syndrome cases and 867 normal group gathered between January 1998 and December 2001. The proportion of Down syndrome observed in this study is about 5﹪far from the population proportion. So in our study, we also evaluate the prior probability problem and compare the difference between
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"Essays on the Modeling of Binary Longitudinal Data with Time-dependent Covariates." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.57363.
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abstract: Longitudinal studies contain correlated data due to the repeated measurements on the same subject. The changing values of the time-dependent covariates and their association with the outcomes presents another source of correlation. Most methods used to analyze longitudinal data average the effects of time-dependent covariates on outcomes over time and provide a single regression coefficient per time-dependent covariate. This denies researchers the opportunity to follow the changing impact of time-dependent covariates on the outcomes. This dissertation addresses such issue through the use of partitioned regression coefficients in three different papers.
In the first paper, an alternative approach to the partitioned Generalized Method of Moments logistic regression model for longitudinal binary outcomes is presented. This method relies on Bayes estimators and is utilized when the partitioned Generalized Method of Moments model provides numerically unstable estimates of the regression coefficients. It is used to model obesity status in the Add Health study and cognitive impairment diagnosis in the National Alzheimer’s Coordination Center database.
The second paper develops a model that allows the joint modeling of two or more binary outcomes that provide an overall measure of a subject’s trait over time. The simultaneous modelling of all outcomes provides a complete picture of the overall measure of interest. This approach accounts for the correlation among and between the outcomes across time and the changing effects of time-dependent covariates on the outcomes. The model is used to analyze four outcomes measuring overall the quality of life in the Chinese Longitudinal Healthy Longevity Study.
The third paper presents an approach that allows for estimation of cross-sectional and lagged effects of the covariates on the outcome as well as the feedback of the response on future covariates. This is done in two-parts, in part-1, the effects of time-dependent covariates on the outcomes are estimated, then, in part-2, the outcome influences on future values of the covariates are measured. These model parameters are obtained through a Generalized Method of Moments procedure that uses valid moment conditions between the outcome and the covariates. Child morbidity in the Philippines and obesity status in the Add Health data are analyzed.<br>Dissertation/Thesis<br>Doctoral Dissertation Statistics 2020
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FANG, HSIN, and 方昕. "A Study on the Influence of Business Discontinued by Using the Binary Logistic Regression Model: Based on the Data of Global Entrepreneurship Monitor." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/71291120073027099759.
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碩士<br>輔仁大學<br>統計資訊學系應用統計碩士在職專班<br>105<br>Presently, “Global Entrepreneurship Monitor” is quite a big-scale international project in the field of entrepreneurship research. In order to do research, they develop both adult population survey and national expert survey to collect data of individual level and national level from those participating countries.
And discover two things below from related references in the past. First one is that both background factors and entrepreneurial attitudes in individual level had significant influences on entrepreneurship activities. And background factors were the intervening variable to effect entrepreneurship activities through entrepreneurial attitudes. The others is that in national level, government policy, enterprise education and social capital also had significant influences on entrepreneurship activities.
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YOU, YI-LING, and 游奕怜. "A Study on the Binary Logistic Regression Prediction Model of Bond Funds Potential Investors – Taking the Certificate Depositors of Bank A as the Example." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/af9sve.
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碩士<br>輔仁大學<br>統計資訊學系應用統計碩士班<br>106<br>Affected by factors such as an aging society, micro-profits, and annuity reform, the risk aversion of Taiwanese investors is rising. Mutual fund has become a ubiquitous financial tool and is widely used by domestic investors. However, Taiwan’s financial institutions are now in a highly competitive situation. This is because there are a great number of financial institutions, and there’s a high degree of homogeneity among them. The aim of this study is to assist the banking industry in holding onto its existing customers and to promote the mutual fund market further.
This study builds a model to determine whether to invest in bond funds or not. It applies Binary Logistic Regression and Ensemble Learning methods to 1,750 certificate depositors of Bank A. After that, the values it predicts and generates will be investigated in groups. The results indicate that the model has nine important influencing factors. Further, Bank A can promote bond funds based on the different characteristics of investors.
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HSIOA, I.-TING, and 蕭依婷. "A Study on the Stock Funds Prediction Models against Wealth Management of Customer Segmentation Using Binary Logistic Regression - Taking Bank A as the Example." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q5dja9.
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碩士<br>輔仁大學<br>統計資訊學系應用統計碩士班<br>106<br>In recent years, the liberalization and internationalization of our country’s economy has led to intense competition in the banking industry. Wealth management has become a common goal for banks, as is evident in their annually increasing demand for financial planners. In the context of investors lacking information and knowledge about financial instruments, the most suitable wealth management instruments for them are mutual funds. Therefore, the primary purpose of this study is to determine how to engage in effective customer relationship management for high-value customers to help enterprises reduce capital costs and increase profits and achieve the purpose of promoting the market.
This study considers 2,630 wealth management customers of Bank A, segmenting them according to their assets under management. The logistic regression model was used to create a prediction model of stock funds to explore the main factors influencing each customer group to invest in stock funds, and also having an interval discussion on model prediction probability for industry’s reference to assess marketing costs and benefits.
The result of the research shows that the main factors influencing silver customers to invest in stock funds are “the number of children,” “job,” “investment cash flows from idle fund,” “risk tolerance and a way to cope with loss,” and “ever held or currently hold a foreign currency deposit.” For gold customers, the factors influencing investors’ stock fund purchases are “job,” “the currently available investment amount,” “customers’ experiences in finance and investment,” “risk tolerance,” “ever held or currently hold stock,” “certificate of deposit or foreign currency deposit,” and “increasing wealth as the purpose of financial management.” For platinum customers, the factors influencing investors’ stock fund purchases are “risk tolerance,” “ever held or currently hold stock, foreign currency deposit, or investment link product.”
According to the results of this study, implications of the findings will be further discussed and the suggestion on the further research in terms of theory and practice will be made as well.
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He, Shin-Ru, and 何欣儒. "The Study on the Effect of Rapid Delivery and Super Merchants' Pickup Services of Frozen Food Online Shopping on Purchasing Behavior-Establishment and Application of Binary logistic Regression Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/updg7j.
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碩士<br>銘傳大學<br>企業管理學系<br>106<br>Taiwan's online shopping market is facing rapid growth and potential of huge opportunities for frozen foods, manufacturers provide fast shipping and super-business pickup can effectively enhance the probability of consumers buying almost no doubt. However, the provision of express delivery will result in higher logistics handling and delivery costs. cooperation with super-operators will incur extra costs for pumping services. For manufacturers, how to evaluate the benefits of rapid shipping and super-merchant pickup, making trade-offs and correct decisions between benefits and costs will become an important issue. In this study, we use stated preference patterns to determine the coefficients and the degree of influence of various variables through the binary logistic regression model, based on the result of the consumers' online shopping solutions for scenario simulation. The study found that the higher the price, the lower the probability of purchase; providing 24-hour delivery service and super-merchant pick-up service can indeed effectively improve the purchase probability. In addition, this study found that providing 24-hour delivery service is equivalent to 87% of the price-competitive ratio. Similarly, the price-to-price ratio of providing super-merchant pick-up service is 9%.
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Wang, Yi-Ching, and 王逸靖. "A Study of the Impact Factors on the Preference of Taiwan Scenic Spots' for Foreign Tourists - Based on Binary Logistic Regression Probabilistic Prediction and Evaluation Models." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3a5b6m.
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碩士<br>輔仁大學<br>統計資訊學系應用統計碩士班<br>106<br>In 2016, the number of Chinese tourists declined by up to 20% as a result of the economic policy related to trade with the Taiwan Strait and economic recession. In the same year, the New Southbound Policy was implemented to increase tourism revenues. Therefore, this research determines foreign tourists’ characteristics and demand, using data from the Republic of Chinas 2016 “Annual Survey of Visitors Expenditure and Trends in Taiwan” conducted by the Ministry of Transportation and Communications. The study aims to facilitate the development of Taiwan’s economy and tourism industry.
Taipei’s five most popular tourist attractions are analyzed and different foreign tourist characteristics are examined using data mining. Finally, decision tree rules are analyzed to recommend an appropriate itinerary for each foreign tourist segment. An appropriate marketing strategy should provide tourists with references needed to organize their itinerary.
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Vering, Steffen. "Scaling credit decisions in FinTech : overcoming boundaries through behavioural credit risk models." Master's thesis, 2019. http://hdl.handle.net/10362/62618.
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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>The decision whom to grant a credit is of utmost importance for financial institutions
in order to develop both financially profitable, as well as widely accessible
financial products. To do this, companies have to be able to distinguish credit
applicants, who are able and likely to pay back their loan, from those, who will
be unable or unwilling to do so in the future. To improve this decision in the
future, the integration of additional behavioural data into the credit decision is
proposed in this thesis.
FinTech firms are increasingly moving interactions between financial institutions
and their customers from local bank branches into digital environments. This
transformation enables companies to gather and analyze a large set of previously
unavailable behavioural indicators, which can help estimate an individuals credit
default risk. This study presents the transforming market conditions that FinTech
firms operate in from a regulatory, technical and behavioural perspective and
outlines the key changes that impact the offering of credit products. Additionally,
it presents the leading approaches of consumer credit research and leverages their
best practices in the creation of a behavioural risk scoring model for a FinTech
company.
The evaluation of the model shows that the inclusion of behavioural indicators
into the credit decision is able to significantly improve the performance of decision
tree based credit risk models. Models trained with additional behavioural data are
able to outperform the base variable set in all performed tests, when compared
using the AUC and Kolmogorov-Smirnov measures, while showing no change
when assessed using the Brier-Score.
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Mathada, Humphrey. "Development of guidelines for dealing with morphological and environmental impacts of sand mining along the Nzhelele River, Limpopo Province of South Africa." Diss., 2015. http://hdl.handle.net/11602/221.
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Peng, Shu-Zhen, and 彭淑珍. "LOGISTIC REGRESSION WITH MISSING COVARIATE FOR CORRELATED BINARY RESPONSE." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/09778343659605031180.
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Huang, Yu-Chieh, and 黃郁潔. "Logistic Regression Analysis of Binary Traits Using Sib-Pairs Data." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/16489330134261433125.
Full textAbstract:
碩士<br>輔仁大學<br>應用統計學研究所<br>93<br>In genetics, it is not an easy work to solve the problems about QTL (quantitative trait locus) mapping. Haseman and Elston (1972) proposed a method for detecting a relationship of linkage between QTL and maker gene locus by using sib-pairs data and linear relationship between the expectation proportion of the squared trait difference (SQD) of sibs and the estimated proportion of alleles shared identical by descent (i.b.d.) at the marker locus. After testing, if the slope was significant negative, there had a meaning showed the higher of the i.b.d. scores, the closer of trait values of the sibs. And we can also find out if there existed a kind of relationship of linkage between QTL and maker gene locus. In this research, we proposed to use a logistic regression method and the rationale of Haseman-Elston method to perform binary trait loci mapping. From our simulation results, it is found that the method proposed here has best power performance under multiplicative model, and the worse power performance under recessive model. The powers under addictive and dominant models are in the second and third places.