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010   $a981-10-0077-8
024 7 $a10.1007/978-981-10-0077-5
035   $a(CKB)4100000007598419
035   $a(DE-He213)978-981-10-0077-5
035   $a(MiAaPQ)EBC5675620
035   $a(PPN)235002356
035   $a(EXLCZ)994100000007598419
100   $a20190204d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aLongitudinal Data Analysis $eAutoregressive Linear Mixed Effects Models /$fby Ikuko Funatogawa, Takashi Funatogawa
205   $a1st ed. 2018.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2018.
215   $a1 online resource (X, 141 p. 27 illus.) 
225 1 $aJSS Research Series in Statistics,$x2364-0065
311 08$a981-10-0076-X 
327   $aChapter 1. Linear mixed effects model -- Chapter 2. Autoregressive linear mixed effects model -- Chapter 3. Bivariate longitudinal data -- Chapter 4. State-space representation -- Chapter 5. Missing data, time dependent covariate -- Chapter 6. Pretest-Posttest data.
330   $aThis book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.
410  0$aJSS Research Series in Statistics,$x2364-0065
606   $aStatistics
606   $aStatistics
606   $aMathematical statistics$xData processing
606   $aStatistical Theory and Methods
606   $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
606   $aStatistics and Computing
615  0$aStatistics.
615  0$aStatistics.
615  0$aMathematical statistics$xData processing.
615 14$aStatistical Theory and Methods.
615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aStatistics and Computing.
676   $a519.5
700   $aFunatogawa$b Ikuko$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767988
702   $aFunatogawa$b Takashi$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910311940603321
996   $aLongitudinal Data Analysis$92163214
997   $aUNINA