Overview
- Describes a new analytical approach for longitudinal data, autoregressive linear mixed effects models, in which dynamic models are induced by the auto-regression term
- Provides state space representation of autoregressive linear mixed models with the modified Kalman filter for the calculation of log likelihoods
- Is written in plain English dealing not only with topics for those in medical fields but that is also understandable for researchers in other disciplines
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
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Table of contents(6 chapters)
About this book
Authors and Affiliations
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Department of Statistical Data Science, The Institute of Statistical Mathematics, Tachikawa, Japan
Ikuko Funatogawa
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Clinical Science and Strategy Department, Chugai Pharmaceutical Co. Ltd., Chūō, Japan
Takashi Funatogawa
About the authors
Takashi Funatogawa, Chugai Pharmaceutical Co. Ltd.
Bibliographic Information
Book Title: Longitudinal Data Analysis
Book Subtitle: Autoregressive Linear Mixed Effects Models
Authors: Ikuko Funatogawa, Takashi Funatogawa
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-981-10-0077-5
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2018
Softcover ISBN: 978-981-10-0076-8Published: 22 February 2019
eBook ISBN: 978-981-10-0077-5Published: 04 February 2019
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: X, 141
Number of Illustrations: 27 b/w illustrations
Topics: Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics and Computing/Statistics Programs