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 An Exploration of Link Functions Used in Ordinal Regression

2020 ◽  
Vol 18 (1) ◽  
pp. 2-15
Author(s):  
Thomas J. Smith ◽  
David A. Walker ◽  
Cornelius M. McKenna
Keyword(s):  
Survey Data ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Simulated Data ◽  
Ordinal Regression ◽  
Research Center ◽  
Link Function ◽  
25Th Anniversary ◽  
Link Functions ◽  
The Web

The purpose of this study is to examine issues involved with choice of a link function in generalized linear models with ordinal outcomes, including distributional appropriateness, link specificity, and palindromic invariance are discussed and an exemplar analysis provided using the Pew Research Center 25th anniversary of the Web Omnibus Survey data. Simulated data are used to compare the relative palindromic invariance of four distinct indices of determination/discrimination, including a newly proposed index by Smith et al. (2017).

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.

scholarly journals A Quasi-Likelihood Approach to Nonnegative Matrix Factorization

2016 ◽  
Vol 28 (8) ◽  
pp. 1663-1693 ◽  
Author(s):  
Karthik Devarajan ◽  
Vincent C. K. Cheung
Keyword(s):  
Matrix Factorization ◽  
Goodness Of Fit ◽  
Nonnegative Matrix Factorization ◽  
Linear Models ◽  
Expectation Maximization Algorithm ◽  
Nonnegative Matrix ◽  
Nonlinear Effects ◽  
Biomedical Signal Processing ◽  
Link Functions ◽  
Unified View

A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proved using the expectation-maximization algorithm. In addition, a measure to evaluate the goodness of fit of the resulting factorization is described. The proposed methods allow modeling of nonlinear effects using appropriate link functions and are illustrated using an application in biomedical signal processing.

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