Data Analysis Using Hierarchical Generalized Linear Models with R
Auteurs : Lee Youngjo, Ronnegard Lars, Noh Maengseok
Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing.
This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
Introduction.
GLMs via iterative weighted least squares.
Inference for models with unobservables.
HGLMs: from Method to Algorithm.
HGLM modelling in R.
Double HGLMS - using the dhglm package.
Fitting multivariate HGLMs.
Survival analysis.
Joint models.
Further Topics.
Youngjo Lee is a professor in the department of Statistics at Seoul National University, Korea. His current research interests are extension, application, theory and software developments for HGLMs.
Lars Rönnegård is affiliated with the Microdata Analysis group at Dalarna University, Sweden. His current research interests are applications of HGLMs in genetics and ecology, and computational aspects.
Maengseok Noh is a professor in the Department of Statistics at Pukyong National University, Korea. His current research interests are application and software developments for HGLMs.
Date de parution : 09-2020
15.6x23.4 cm
Date de parution : 06-2017
15.6x23.4 cm
Thème de Data Analysis Using Hierarchical Generalized Linear... :
Mots-clés :
REML Log Likelihood; IWLS Algorithm; random effects; Additive Non-parametric Regression Model; multivariate; Adjusted Profile Likelihood; survival; Linear Mixed Model; likelihood; GLM Model; Bayesian; REML Estimator; fixed effects; Poisson GLM; Lars Rönnegård; Frailty Model; Maengseok Noh; Error T-value; Epileptic Seizure Data; Ml Estimator; Condition AIC; REML Likelihood; Min 1Q Median 3Q Max; Non-parametric Baseline Hazard; Likelihood Function Values; Baseline Hazard; Unspecified Baseline Hazard Function; REML Estimate; Normal Probability Plot; Gamma Frailty Model; Log Normal Frailty; Discrete Random Effects; Poisson GLMM