Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects - Info and Reading Options
By James S. Hodges
"Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects" was published by CRC Press in 2014 - Boca Raton and it has 431 pages.
“Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects” Metadata:
- Title: ➤ Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects
- Author: James S. Hodges
- Number of Pages: 431
- Publisher: CRC Press
- Publish Date: 2014
- Publish Location: Boca Raton
“Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects” Subjects and Themes:
- Subjects: ➤ Mathematical statistics - Regression analysis - Textbooks - Linear models (Statistics) - MATHEMATICS / Probability & Statistics / General - Regression Analysis - Linear Models - Analyse de régression - Modèles linéaires (Statistique) - MATHEMATICS - Applied - Probability & Statistics - General
Edition Identifiers:
- The Open Library ID: OL26131434M - OL17541555W
- Online Computer Library Center (OCLC) ID: 868795497
- Library of Congress Control Number (LCCN): 2013026053
- ISBN-13: 9781439866832
- All ISBNs: 9781439866832
AI-generated Review of “Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects”:
"Richly Parameterized Linear Models Additive Time Series And Spatial Models Using Random Effects" Description:
Open Data:
"This book covers a wide range of statistical models, including hierarchical, hierarchical generalized linear, linear mixed, dynamic linear, smoothing, spatial, and longitudinal. It presents a framework for expressing these richly parameterized models together as well as tools for exploring and interpreting the results of fitting the models to data. It extends the standard theory of linear models and illustrates the advantages and disadvantages of various theories. The book also examines surprising or undesirable results arising in the use of the models to analyze real data sets from collaborative research"--
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