Results 241 to 250 of about 208,599 (260)

Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression

Biometrika, 1986
The paper studies robust M-estimation for generalized linear models, thereby extending results of \textit{W. S. Krasker} and \textit{R. E. Welsch} [J. Am. Stat. Assoc. 77, 595-604 (1982; Zbl 0501.62062)] for the linear model. A (quasi-) score function is proposed which possesses certain optimality properties with respect to robustness and efficiency ...
Stefanski, Leonard A., Carroll, Raymond J., Ruppert, David   +2 more
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More on the generalized linear model: logistic and loglinear models

2019
This chapter discusses sub-frameworks of the generalized linear model (GLZM) which can be used for nominal-level data with two categories (binary logistic model) and scale data in the form of counts (loglinear model). It examines how they can be used to assess the relative effects of multiple explanatory (independent) variables on a single response ...
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Effects of the housing price to income ratio on tenure choice in Taiwan: forecasting performance of the hierarchical generalized linear model and traditional binary logistic regression model

Journal of Housing and the Built Environment, 2017
This study examined factors that influence the tenure choices of households in different counties and cities of Taiwan. Data collected in the Housing Status Survey by the Construction and Planning Agency of the Ministry of the Interior were analyzed using hierarchical generalized linear modeling (HGLM).
Chun-Chang Lee, Chih-Min Liang, Jian-Zheng Chen, Cheng-Huang Tung   +3 more
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[Logistic regression vs other generalized linear models to estimate prevalence rate ratios].

Revue d'epidemiologie et de sante publique, 2000
In cross-sectional studies, to quantify the association between a risk factor and a disease (possibly adjusted for confounders), in the framework of the multiplicative model, the more obvious effect measure is a prevalence rate ratio with an associated confidence interval.
P, Traissac, Y, Martin-Prével, F, Delpeuch, B, Maire   +3 more
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PREDICTING MATHEMATICAL ANXIETY: A COMPARATIVE ANALYSIS OF RANDOM FOREST, LOGISTIC REGRESSION, AND HIERARCHICAL GENERALIZED LINEAR MODELS USING PYTHON

МАТЕМАТИКА, ФИЗИКА ЖӘНЕ ИНФОРМАТИКАНЫ ОҚЫТУДЫҢ ӨЗЕКТІ МӘСЕЛЕЛЕРІ
Math anxiety is a significant concern for many educators and policymakers due to its negative impact on students' math performance and career prospects. Various empirical studies have been conducted to examine the factors predicting math anxiety, typically based on a limited set of predefined variables, such as math performance and students' self ...
Mustafa ÖZKAN, G.K. Yeshmurat, L.S. Kainbayeva   +2 more
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Phrase break prediction using logistic generalized linear model

Interspeech 2006, 2006
Lifu Yi, Jian Li, Xiaoyan Lou, Jie Hao
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Multicriteria emergency decision for responding to man overboard casualties: A proposed procedure developed using Binary Logistics Regression and General Linear Models

Ocean Engineering, 2022
Orhan Gonel, Ismail Cicek
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The Generalized Linear Model: Logistic and Poisson Regression

2018
Scott A. Pardo, Michael A. Pardo
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Evaluating Performance Of Identifying At - Risk Students And Learning Achievement Model using Accuracy And F-measure by Comparing Logistic Regression, Generalized Linear Model And Gradient Boost Machine Algorithm

2022 International Conference for Advancement in Technology (ICONAT), 2022
K. Harini, K K. Sashi Rekha
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Logistic Regression and Generalized Linear Models for Binary Survey Variables

2010
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