Results 221 to 230 of about 58,672 (256)

The performance of M-based generalized linear model (GLM) procedures based on the coverage probability

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
In designed experiments, we often encountered nonnormal response variables. The data transformations (Transf) approached are frequently employed to deal with these problems. One has to realize that analyzing such data based on transformations posed many drawbacks. A better approach in dealing with these problems is by using the Generalized Linear Model
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General Linear Model (GLM) Multivariate Analysis

2006
exaly   +4 more sources

GLM+: An Efficient System for Generalized Linear Models

2018 IEEE International Conference on Big Data and Smart Computing (BigComp), 2018
Generalized linear models are widely used in data analysis and machine learning, especially in large-scale machine learning because of its simplicity and good performance. Generalized linear models include regression, like linear regression, lasso and classification, support vector machine and logistic regression.
Lele Yu, Lingyu Wang, Yingxia Shao, Long Guo, Bin Cui 0001   +4 more
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General Linear Model (GLM) Multivariate Analysis

2013
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Generalized Linear Models (GLM)

2021
Abstract Chapter 7 introduces one of the most useful statistical frameworks for the modern life scientist: the generalized linear model (GLM). GLMs extend the linear model to an array of non-normally distributed data such as Poisson, negative binomial, binomial, and Gamma distributed data. These models dramatically improve the breadth of
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Introduction to the generalized linear model (GLM): comparing two groups in a Poisson regression

Marc Kéry, Kenneth F. Kellner
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Generalized Linear Models (GLMs)

2019
Generalized Linear Models are widely known under their famous acronym GLMs. Today, GLMs are recognized as an industry standard for pricing personal lines and small commercial lines of insurance business. This chapter reviews the GLM methodology with a special emphasis to insurance problems.
Michel Denuit, Donatien Hainaut, Julien Trufin   +2 more
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Using Generalized Linear Models (GLMs) to Model Errors in Motor Performance

Journal of Motor Behavior, 1991
Because of differences in design factors, experiments in human motor performance sometimes produce a wide range in variability or consistency in a subject's individual errors. These differences in variation often lead to heterogeneity in the variance-covariance matrices between group factors, which prohibits the use of repeated-measures (RM) ANOVA or ...
A M, Nevill, J B, Copas
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On the use and interpretation of Generalized Linear Models (GLMs) in agricultural sciences: critical analysis and application guidelines

African Journal of Applied Statistics, 2023
Generalized Linear Models (GLMs) provide a unified approach that encompasses most of the linear models. However, they could not been properly used in agricultural sciences. In this review, we (1) examined the correctness related to the application of GLMs to agricultural sciences; and (2) provided a guideline for their suitable use.
Paulette Béhanzin Guédézoumè, Scholastique Merveille Essetcheou, Bruno Enagnon Lokonon   +2 more
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Analysis and Interpretation of the Particulate Matter Concentrations at the Subway Stations by General Linear Model (GLM) and Correlation Analysis

2021
The particulate matters (PM10 and PM2.5) inside urban subway stations greatly influence indoor air quality and passenger comfort. This study aims to analyze and interpret the concentrations of PM10 and PM2.5, measured in several subway stations from October 9th to 22nd, 2016 in Beijing, China.
Xinru Wang, Song Pan, Xingxing Zhang, Li Chang, Yiqiao Liu   +4 more
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