Generalized Linear Models with Random Effects - Info and Reading Options
Unified Analysis via H-likelihood
By Youngjo Lee
"Generalized Linear Models with Random Effects" was published by Taylor & Francis Group in 2006 - Boca Raton, FL, the book is classified in Mathematics genre, it has 411 pages and the language of the book is English.
“Generalized Linear Models with Random Effects” Metadata:
- Title: ➤ Generalized Linear Models with Random Effects
- Author: Youngjo Lee
- Language: English
- Number of Pages: 411
- Is Family Friendly: Yes - No Mature Content
- Publisher: Taylor & Francis Group
- Publish Date: 2006
- Publish Location: Boca Raton, FL
- Genres: Mathematics
“Generalized Linear Models with Random Effects” Subjects and Themes:
- Subjects: ➤ Equations d'estimation ge ne ralise es - Linear models (Statistics) - Generalized estimating equations - Linear models (statistics) - Linear Models - Équations d'estimation généralisées - Modèles linéaires (Statistique) - MATHEMATICS - Probability & Statistics - General - Applied
Edition Specifications:
- Pagination: 416
Edition Identifiers:
- Google Books ID: JjDMeRM4woEC
- The Open Library ID: OL34585957M - OL16916295W
- ISBN-13: 9781420011340
- ISBN-10: 1420011340
- All ISBNs: 9781420011340 - 1420011340
AI-generated Review of “Generalized Linear Models with Random Effects”:
Snippets and Summary:
Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement ...
"Generalized Linear Models with Random Effects" Description:
Google Books:
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.
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- Public Domain: No
- Availability Status: Partially available
- Availability Status for country: US.
- Available Formats: Text is not avialbe, image copy is available.
- Google Books Link: Google Books
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