Relevant bibliographies by topics / Binary Logistic Regression Model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Binary Logistic Regression Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Binary Logistic Regression Model"

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Midi, Habshah, S. K. Sarkar, and Sohel Rana. "Collinearity diagnostics of binary logistic regression model." Journal of Interdisciplinary Mathematics 13, no. 3 (June 2010): 253–67. http://dx.doi.org/10.1080/09720502.2010.10700699.

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Dombi, József, and Tamás Jónás. "Kappa Regression: An Alternative to Logistic Regression." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 02 (April 2020): 237–67. http://dx.doi.org/10.1142/s0218488520500105.

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In this study, a new regression method called Kappa regression is introduced to model conditional probabilities. The regression function is based on Dombi’s Kappa function, which is well known in fuzzy theory. Here, we discuss how the Kappa function relates to the Logistic function as well as how it can be used to approximate the Logistic function. We introduce the so-called Generalized Kappa Differential Equation and show that both the Kappa and the Logistic functions can be derived from it. Kappa regression, like binary Logistic regression, models the conditional probability of the event that a dichotomous random variable takes a particular value at a given value of an explanatory variable. This new regression method may be viewed as an alternative to binary Logistic regression, but while in binary Logistic regression the explanatory variable is defined over the entire Euclidean space, in the Kappa regression model the predictor variable is defined over a bounded subset of the Euclidean space. We will also show that asymptotic Kappa regression is Logistic regression. The advantages of this novel method are demonstrated by means of an example, and afterwards some implications are discussed.
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Kuha, Jouni, and Colin Mills. "On Group Comparisons With Logistic Regression Models." Sociological Methods & Research 49, no. 2 (January 7, 2018): 498–525. http://dx.doi.org/10.1177/0049124117747306.

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It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the second when models are compared between groups that have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is really the subject of substantive interest. If it is, the problem should be addressed through better measurement of this response. The second concern refers to a situation which is unavoidable but unproblematic, in that causal effects and descriptive associations are inherently group dependent and can be compared as long as they are correctly estimated.
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Gbohounme, Idelphonse Leandre Tawanou, Oscar Owino Ngesa, and Jude Eggoh. "Self-Selecting Robust Logistic Regression Model." International Journal of Statistics and Probability 6, no. 3 (May 14, 2017): 132. http://dx.doi.org/10.5539/ijsp.v6n3p132.

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Logistic regression model is the most common model used for the analysis of binary data. However, the problem of atypical observations in the data has an unduly effect on the parameter estimates. Many researchers have developed robust statistical model to solve this problem of outliers. Gelman (2004) proposed GRLR, a robust model by trimming the probability of success in LR. The trimming values in this model were fixed and the user is required to specify this value well in advance. In particular this study developed SsRLR model by allowing the data itself to select the alpha value. We proposed a Restricted LR model to substitute the LR in presence of outliers. We proved that the SsRLR model is the more robust to the presence of leverage points in the data. Parameter estimations is done using a full Bayesian approach implemented in WinBUGS 14 software.
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Shrestha, Noora. "Assessing Discriminatory Performance of a Binary Logistic Regression Model." International Journal of Advances in Scientific Research and Engineering 5, no. 7 (2019): 194–98. http://dx.doi.org/10.31695/ijasre.2019.33448.

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YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (December 2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.
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Dukalang, Hendra H. "PERBANDINGAN REGRESI LOGISTIK BINER DAN PROBIT BINER DALAM PEMODELAN TINGKAT PARTISIPASI ANGKATAN KERJA." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 7, no. 2 (December 30, 2019): 62–70. http://dx.doi.org/10.34312/euler.v7i2.10355.

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Regression is a data analysis method used to model the relationship between one response variable and one or more predictor variables. In regression modelling, data is often used. In general, the regression model that is often used is simple or multiple regression in modelling where the response variable is quantitative data. The fundamental difference from regression models using quantitative data is the main objective is to estimate the average value of the dependent variable using certain values of the independent variable. Whereas in a regression model with a qualitative dependent variable the main objective is to find the probability of something happening (probability model). One of the development methods of the regression model for data with qualitative response variables is Logistic and Probit regression. The purpose of this study was to compare the best model using binary logistic regression with binary probit regression in the case of Labor Force Participation Rate (TPAK) in Gorontalo City. The research method used is quantitative research methods, with binary logistic regression modelling and binary probit regression. The results showed that the variable that has a significant effect on TPAK Gorontalo City is the open unemployment rate, and the best model between the binary logistic regression model with an AIC value of 1.289 is smaller than the AIC value of the binary Probit regression 1.318, likewise from the R2 value the R2 value for regression is obtained. binary logistic of 12.74%, greater than the R2 value of binary probit regression of 10.70%.
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Kubkowski, Mariusz, and Jan Mielniczuk. "Projections of a general binary model on a logistic regression." Linear Algebra and its Applications 536 (January 2018): 152–73. http://dx.doi.org/10.1016/j.laa.2017.09.013.

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McCormick, Tyler H., Adrian E. Raftery, David Madigan, and Randall S. Burd. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification." Biometrics 68, no. 1 (August 12, 2011): 23–30. http://dx.doi.org/10.1111/j.1541-0420.2011.01645.x.

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Sarkar, S. K., and Habshah Midi. "Importance of Assessing the Model Adequacy of Binary Logistic Regression." Journal of Applied Sciences 10, no. 6 (March 1, 2010): 479–86. http://dx.doi.org/10.3923/jas.2010.479.486.

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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.

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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.
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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.
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.
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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.
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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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.
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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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?

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.

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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
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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The 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.

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Books on the topic "Binary Logistic Regression Model"

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Loftsgaarden, Don O. Constructing and testing logistic regression models for binary data: Applications to the national fire danger rating system. Ogden, Utah: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1992.

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Loftsgaarden, Don O. Constructing and testing logistic regression models for binary data: Applications to the National Fire Danger Rating System. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1992.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. Iowa City, Iowa: ACT, Inc., 1997.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. Iowa City, Iowa: ACT, Inc., 1997.

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Some aspects of statistical inference of logistic regression model parameters. Uppsala: Acta Universitatis Upsaliensis, 1996.

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Thompson, Norris B., and SreyRam Kuy. Multivariable Predictors of Postoperative Surgical Site Infection after General and Vascular Surgery. Edited by SreyRam Kuy. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0013.

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This landmark study proposed a model for predicting surgical site infections (SSI). Using logistic regression analysis, variables independently associated with increased risk of SSI were identified, which included smoking, alcohol use, comorbidities, disseminated cancer, weight loss greater than 10%, emergency surgery, and length of operative time. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case.
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Book chapters on the topic "Binary Logistic Regression Model"

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Wilson, Jeffrey R., and Kent A. Lorenz. "Standard Binary Logistic Regression Model." In ICSA Book Series in Statistics, 25–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23805-0_3.

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Dobson, Annette J. "Binary variables and logistic regression." In An Introduction to Generalized Linear Models, 104–22. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_8.

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Harrell, Frank E. "Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death." In Regression Modeling Strategies, 275–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_11.

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Kumari, Dipti, and Kumar Rajnish. "Comparing Efficiency of Software Fault Prediction Models Developed Through Binary and Multinomial Logistic Regression Techniques." In Advances in Intelligent Systems and Computing, 187–97. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2250-7_19.

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Sreejesh, S., Sanjay Mohapatra, and M. R. Anusree. "Binary Logistic Regression." In Business Research Methods, 245–58. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00539-3_11.

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Harrell, Frank E. "Binary Logistic Regression." In Regression Modeling Strategies, 215–67. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_10.

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Maroof, David Aaron. "Binary Logistic Regression." In Statistical Methods in Neuropsychology, 67–75. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3417-7_8.

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Pardo, Scott A. "Binary Logistic Regression." In Empirical Modeling and Data Analysis for Engineers and Applied Scientists, 145–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32768-6_10.

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Harrell, Frank E. "Binary Logistic Regression." In Regression Modeling Strategies, 219–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_10.

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Matthews, David Edward, and Vernon Todd Farewell. "11 Binary Logistic Regression." In Using and Understanding Medical Statistics, 128–40. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099426.

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Conference papers on the topic "Binary Logistic Regression Model"

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Lavazza, Luigi, and Sandro Morasca. "Dealing with Uncertainty in Binary Logistic Regression Fault-proneness Models." In EASE '19: Evaluation and Assessment in Software Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319008.3319012.

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"Analysis and Prediction of P2P Online Lending Platform—Based on Binary Logistic Regression Model." In 2017 the 7th International Workshop on Computer Science and Engineering. WCSE, 2017. http://dx.doi.org/10.18178/wcse.2017.06.224.

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Abdelrhman, Ahmed M., Lim Ying, Y. H. Ali, Iftikhar Ahmad, Christina G. Georgantopoulou, Fethma M. Nor, and Denni Kurniawan. "Diagnosis model for bearing faults in rotating machinery by using vibration signals and binary logistic regression." In 1ST INTERNATIONAL SEMINAR ON ADVANCES IN METALLURGY AND MATERIALS (i-SENAMM 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0017221.

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Agaskar, Ameya, and Yue M. Lu. "ALARM: A logistic auto-regressive model for binary processes on networks." In 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2013. http://dx.doi.org/10.1109/globalsip.2013.6736876.

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Vladeanu, Greta Julia, and Dan D. Koo. "A Comparison Study of Water Pipe Failure Prediction Models Using Weibull Distribution and Binary Logistic Regression." In Pipelines 2015. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479360.146.

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Liu, Zhui, Honglu Gou, and Lingying Kong. "Survey on turnover intention of scientific and technological workers based on the binary logistic regression model—a case study of XPCC." In International Conference on Information Management and Management Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/imme140591.

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Shariff, S. Sarifah Radiah, Nur Atiqah Mohd Rodzi, Kahartini Abdul Rahman, Siti Meriam Zahari, and Sayang Mohd Deni. "Predicting the “graduate on time (GOT)” of PhD students using binary logistics regression model." In THE 4TH INTERNATIONAL CONFERENCE ON QUANTITATIVE SCIENCES AND ITS APPLICATIONS (ICOQSIA 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4966105.

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Suliyanto, Marisa Rifada, and Eko Tjahjono. "Estimation of nonparametric binary logistic regression model with local likelihood logit estimation method (case study of diabetes mellitus patients at Surabaya Hajj General Hospital)." In SYMPOSIUM ON BIOMATHEMATICS 2019 (SYMOMATH 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0025807.

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Bastidas Zelaya, Efrain. "Analysis of multistage chains in public transport: The case of Quito, Ecuador." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3530.

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Because of the growth of cities in size and population, people get used to perform several stage trips involving transfers due to advantages such as time or price paid, being multistage trips more attractive compared to single stage trips. In Quito, Ecuador, nowadays multistage trips represent one third of total daily trips. This paper seeks to identify main characteristics of multistage trips as well as find relationships and inferences that allow recommendations regarding best practices to policy makers and transport managers. The information used belong to the data collected in the Household Survey Mobility held in Quito in 2011. Based on these data, the present work starts using an analysis with descriptive statistics. The next phase of this research involves the search for a methodology in order to identify correlations between demographic, socioeconomic and transport variables related with traveler´s choice for making or not a transfer. Best methodology found was the use of Binary Logistic Regression (Logit) and specific computer software, with which different statistic's models were performed to find the strongest correlation. The paper ends with conclusions and recommendations as well as suggestions for future research.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3530
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Vencúrik, Tomáš, Dominik Bokůvka, Jiří Nykodým, and Pavel Vacenovský. "Decision making of semi-professional female basketball players in competitive games." In 12th International Conference on Kinanthropology. Brno: Masaryk University Press, 2020. http://dx.doi.org/10.5817/cz.muni.p210-9631-2020-48.

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Purpose: Nowadays, not only the research but also coaching is focusing on decision making in basketball. Decision making is critical in basketball, especially in relation to offensive skills (with ball). Generally, the players have to decide what to do with the ball (make an appro-priate decision) and in the shortest time possible. From this point of view, the study aims to identify the factors which can affect the decision making of offensive skills of female basket-ball players. Methods: Eight semi-professional female basketball players participated in this study. Basket-ball players played five competitive games in the second division. During all games, the heart rate was monitored. Decision making was assessed according to Basketball Offensive Game Performance Instrument (BOGPI) and categorized as appropriate and inappropriate. For this purpose, the notational analysis was used. Based on previous research, the four main factors were set as independent variables. Each of these factors was categorized. The first factor was the intensity of load ( 95% of HR ), second factor was ball possession duration (0–8 s, 9–16 s, and 17–24 s), third factor was game period (1st quarter, 2nd quarter, 3rd quarter, and 4th quarter), and the fourth factor was defensive pressure of an opponent (low, moderate, and high). Objectivity was verified by the method of inter-rater agreement, and re-liability was using intra-rater agreement. The influence of factors on decision making was ex-pressed by binary logistic regression. Method of backward stepwise selection was used to find predictors of inappropriate decisions and to find the best model. Results: One regression coeficient in the final model was statistically significant – defensive pressure of the opponent. When the defensive pressure is moderate or high, the chance for inappropriate decisions increased. Conclusion: Based on these findings, the coaches should take into consideration these fac-tors when preparing individual training sessions.
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