Results 211 to 220 of about 68,121 (251)

Water temperature modelling: comparison between the generalized additive model, logistic, residuals regression and linear regression models

Hydrological Sciences Journal, 2017
Water temperature has a significant influence on aquatic organisms, including stenotherm fish such as salmonids.
Laanaya, Fatima, St-Hilaire, André, Gloaguen, Erwan   +2 more
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Logistic Regression: The Generalized Linear Model in the Social Sciences

Perceptual and Motor Skills, 1997
A new statistical technique is now being used quite frequently in the behavioral and social sciences but appears to be poorly understood due to undue reliance perhaps on explanations in statistical packages.
Michael C. White, Rebecca G. Long, Richard Tansey   +2 more
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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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Testing equivalence to binary generalized linear models with application to logistic regression

Statistics & Probability Letters, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

The International Journal of Advanced Manufacturing Technology, 2012
Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems.
Mehdi Koosha, Amirhossein Amiri
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Generalized Linear Models I: Logistic Regression

2001
All of the models discussed so far assume that the dependent variable is continuous and normally distributed. In this chapter, we introduce generalized linear models, which include the regression and ANOVA models of previous chapters, but can also be used for modeling non-normally distributed response variables, in particular categorical variables.
Brian Everitt, Sophia Rabe-Hesketh
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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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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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[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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