Letters to the Editor on the computation of likelihood ratio and score test based confidence intervals in generalized linear models

1993 ◽  
Vol 12 (10) ◽  
pp. 989-989
Author(s):  
Juha M. Alho ◽  
Salomon Minkin
Keyword(s):  
Confidence Intervals ◽  
Likelihood Ratio ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Score Test ◽  
Letters To The Editor

Score Test for Linkage in Generalized Linear Models

2007 ◽  
Vol 64 (1) ◽  
pp. 5-15 ◽  
Author(s):  
J.J.P. Lebrec ◽  
H.C. van Houwelingen
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Score Test

scholarly journals Hypothesis Testing in Generalized Linear Models with Functional Coefficient Autoregressive Processes

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
Lei Song ◽  
Hongchang Hu ◽  
Xiaosheng Cheng
Keyword(s):  
Maximum Likelihood ◽  
Hypothesis Testing ◽  
Likelihood Ratio ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Autoregressive Processes ◽  
Pseudo Likelihood ◽  
Quasi Maximum Likelihood ◽  
Functional Coefficient

The paper studies the hypothesis testing in generalized linear models with functional coefficient autoregressive (FCA) processes. The quasi-maximum likelihood (QML) estimators are given, which extend those estimators of Hu (2010) and Maller (2003). Asymptotic chi-squares distributions of pseudo likelihood ratio (LR) statistics are investigated.

Power Calculations for Likelihood Ratio Tests in Generalized Linear Models

Biometrics ◽  
1992 ◽  
Vol 48 (1) ◽  
pp. 31 ◽  
Author(s):  
Steven G. Self ◽  
Robert H. Mauritsen ◽  
Jill Ohara
Keyword(s):  
Likelihood Ratio ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Likelihood Ratio Tests ◽  
Power Calculations

Covariance Generalized Linear Models: An Approach for Quantifying Uncertainty in Tree Stem Taper Modeling

2021 ◽  
Author(s):  
Luan Demarco Fiorentin ◽  
Wagner Hugo Bonat ◽  
Allan Libanio Pelissari ◽  
Sebastião do Amaral Machado ◽  
Saulo Jorge Téo
Keyword(s):  
Covariance Matrix ◽  
Confidence Intervals ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Mixed Effect Model ◽  
Mixed Effect ◽  
Stem Taper ◽  
Effect Model ◽  
The Mean ◽  
Tree Stem

Abstract A natural dependence among diameters measured within-tree is expected in taper data due to the hierarchical structure. The aim of this paper was to introduce the covariance generalized linear model (CGLM) framework in the context of forest biometrics for Pinus taeda stem form modeling. The CGLMs are based on marginal specification, which requires a definition of the mean and covariance components. The tree stem mean profiles were modeled by a nonlinear segmented model. The covariance matrix was built considering four strategies of linear combinations of known matrices, which expressed the variance or correlations among observations. The first strategy modeled only the variance of the diameters over the stem as a function of covariates, the second modeled correlation among observations, the third was defined based on a random walk model, the fourth was based on a structure similar to a mixed-effect model with a marginal specification, and the fourth was a traditional mixed-effect model. Mean squared error and bias showed that the approaches were similar for describing the mean profile for fitting and validation dataset. However, uncertainties expressed by confidence intervals of the relative diameters were significant and related to the matrix covariance structures of the CGLMs. Study Implications: We proposed stem taper modeling based on a new class of statistical models. Covariance generalized linear models allow quantification of the stem dynamic by using a nonlinear model. Uncertainty estimates are performed on a covariance matrix given by a linear combination of known matrices. The matrices enable modeling of the nonconstant variance as well as the several correlation patterns, resulting in a framework more flexible and robust than traditional approaches usually applied for stem taper modeling. For practical purposes, uncertainty modeling can improve forest management planning, because the production limits by timber assortments are more reliable due to the confidence intervals derived from an appropriate uncertainty analysis.

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