Goodness of fit of generalized linear models to sparse data

2000 ◽  
Vol 62 (2) ◽  
pp. 323-333 ◽  
Author(s):  
S. R. Paul ◽  
D. Deng
Keyword(s):  
Generalized Linear Models ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Sparse Data

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions

2015 ◽  
Vol 58 (3) ◽  
pp. 674-690 ◽  
Author(s):  
Jana D. Canary ◽  
Leigh Blizzard ◽  
Ronald P. Barry ◽  
David W. Hosmer ◽  
Stephen J. Quinn
Keyword(s):  
Generalized Linear Models ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Fit Statistics ◽  
Link Functions

scholarly journals Why analyze germination experiments using Generalized Linear Models?

2018 ◽  
Vol 40 (3) ◽  
pp. 281-287 ◽  
Author(s):  
Fábio Janoni Carvalho ◽  
Denise Garcia de Santana ◽  
Lúcio Borges de Araújo
Keyword(s):  
Generalized Linear Models ◽  
Binomial Distribution ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Binomial Model ◽  
Inappropriate Use ◽  
Copaiba Oil ◽  
Germination Experiments ◽  
Combined Data

Abstract: We compared the goodness of fit and efficiency of models for germination. Generalized Linear Models (GLMs) were performed with a randomized component corresponding to the percentage of germination for a normal distribution or to the number of germinated seeds for a binomial distribution. Lower levels of Akaikes’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) combined, data adherence to simulated envelopes of normal plots and corrected confidence intervals for the means guaranteed the binomial model a better fit, justifying the importance of GLMs with binomial distribution. Some authors criticize the inappropriate use of analysis of variance (ANOVA) for discrete data such as copaiba oil, but we noted that all model assumptions were met, even though the species had dormant seeds with irregular germination.

A Global Test for the Goodness of Fit of Generalized Linear Models : An Estimating Equation Approach

2005 ◽  
Vol 56 (1-4) ◽  
pp. 251-282
Author(s):  
R. Prabhakar Rao ◽  
B.C. Sutradhar
Keyword(s):  
Generalized Linear Models ◽  
Goodness Of Fit ◽  
Exponential Family ◽  
Linear Models ◽  
Estimating Equation ◽  
Goodness Of Fit Test ◽  
Global Test ◽  
Goodness Of Fit Tests ◽  
Exponential Family Of Distributions ◽  
Family Of Distributions

Summary Generalized linear models are used to analyze a wide variety of discrete and continuous data with possible overdispersion under the assumption that the data follow an exponential family of distributions. The violation of this assumption may have adverse effects on the statistical inferences. The existing goodness of fit tests for checking this assumption are valid only for a standard exponential family of distributions with no overdispersion. In this paper, we develop a global goodness of fit test for the general exponential family of distributions which may or may not contain overdispersion. The proposed statistic has asymptotically standard Gaussian distribution which should be easy to implement.

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