Dissertations / Theses on the topic 'Binary Logistic Regression Model'
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Wang, Jie. "Incorporating survey weights into logistic regression models." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/267.
Full textKonis, 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.
Full textZhang, 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.
Full textThesis 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.
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.
Full textPh. D.
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.
Find full textLopez, 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/.
Full textThis 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.
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.
Full textThe 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?
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.
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.
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.
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.
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.
Full textve 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.
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.
Full textAphane, 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.
Full textThe main objective of this study was to identify ways in which transaction costs can be lowered to improve small-scale farmers&rsquo
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
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
25 km and 26 &ndash
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
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
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.
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.
Full textLo, 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.
Full textIncludes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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.
Full textCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
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).
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.
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.
Full textAbel, 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.
Full textLiu, Ying. "On goodness-of-fit of logistic regression model." Diss., Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/530.
Full textHeise, Mark A. "Optimal designs for a bivariate logistic regression model." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38538.
Full textPh. D.
Rodrigues, José Tenylson Gonçalves. "Análise de dados longitudinais para variáveis binárias." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4531.
Full textFinanciadora de Estudos e Projetos
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.
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.
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.
Find full textTitle from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.
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.
Full textO 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.
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.
Zimmer, Zachary. "Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.
Full textSignorini, David F. "Practical aspects of kernel smoothing for binary regression and density estimation." Thesis, n.p, 1998. http://oro.open.ac.uk/19923/.
Full textEldud, 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 textLetsinger, 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/.
Full textLetsinger, William C. II. "Optimal one and two-stage designs for the logistic regression model." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/37354.
Full textPh. D.
Liu, Xiang. "A Multi-Indexed Logistic Model for Time Series." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3140.
Full textStrandberg, 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.
Full textDet 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.
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.
Full textSeries: Research Report Series / Department of Statistics and Mathematics
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.
Full textFu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.
Full textAlkhalaf, 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.
Full textEducation, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
Graduate
Khajuria, Saket. "A Model to Predict Student Matriculation from Admissions Data." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960.
Full textHossain, 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.
Full textMatshego, 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.
Full textMATYATIM, Rosliza. "The Classification Model for Corporate Failures in Malaysia." Graduate School of International Development, Nagoya University, 2006. http://hdl.handle.net/2237/7314.
Full textBelyaeva, 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.
Full textChien, Yung-Lin, and 簡詠霖. "Using Conditional Logistic Regression Model in Classifying Binary Data." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/60527189839530107512.
Full text"Three Essays on Correlated Binary Outcomes: Detection and Appropriate Models." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49005.
Full textDissertation/Thesis
Doctoral Dissertation Statistics 2018
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 textChang, 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.
Full text國立陽明大學
公共衛生研究所
90
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
"Essays on the Modeling of Binary Longitudinal Data with Time-dependent Covariates." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.57363.
Full textDissertation/Thesis
Doctoral Dissertation Statistics 2020
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.
Full text輔仁大學
統計資訊學系應用統計碩士在職專班
105
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.
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.
Full text輔仁大學
統計資訊學系應用統計碩士班
106
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.
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.
Full text輔仁大學
統計資訊學系應用統計碩士班
106
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.
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.
Full text銘傳大學
企業管理學系
106
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%.
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.
Full text輔仁大學
統計資訊學系應用統計碩士班
106
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.
Vering, Steffen. "Scaling credit decisions in FinTech : overcoming boundaries through behavioural credit risk models." Master's thesis, 2019. http://hdl.handle.net/10362/62618.
Full textThe 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.
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.
Full textPeng, Shu-Zhen, and 彭淑珍. "LOGISTIC REGRESSION WITH MISSING COVARIATE FOR CORRELATED BINARY RESPONSE." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/09778343659605031180.
Full textHuang, 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 text輔仁大學
應用統計學研究所
93
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.