語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Longitudinal data analysisautoregres...
~
Funatogawa, Ikuko.
Longitudinal data analysisautoregressive linear mixed effects models /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Longitudinal data analysisby Ikuko Funatogawa, Takashi Funatogawa.
其他題名:
autoregressive linear mixed effects models /
作者:
Funatogawa, Ikuko.
其他作者:
Funatogawa, Takashi.
出版者:
Singapore :Springer Singapore :2018.
面頁冊數:
x, 141 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Longitudinal method.
電子資源:
https://doi.org/10.1007/978-981-10-0077-5
ISBN:
9789811000775$q(electronic bk.)
Longitudinal data analysisautoregressive linear mixed effects models /
Funatogawa, Ikuko.
Longitudinal data analysis
autoregressive linear mixed effects models /[electronic resource] :by Ikuko Funatogawa, Takashi Funatogawa. - Singapore :Springer Singapore :2018. - x, 141 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..
Chapter 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.
This 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.
ISBN: 9789811000775$q(electronic bk.)
Standard No.: 10.1007/978-981-10-0077-5doiSubjects--Topical Terms:
191626
Longitudinal method.
LC Class. No.: QA278 / .F863 2018
Dewey Class. No.: 519.535
Longitudinal data analysisautoregressive linear mixed effects models /
LDR
:02700nmm a2200337 a 4500
001
548311
003
DE-He213
005
20190515160347.0
006
m d
007
cr nn 008maaau
008
190729s2018 si s 0 eng d
020
$a
9789811000775$q(electronic bk.)
020
$a
9789811000768$q(paper)
024
7
$a
10.1007/978-981-10-0077-5
$2
doi
035
$a
978-981-10-0077-5
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA278
$b
.F863 2018
072
7
$a
PBT
$2
bicssc
072
7
$a
MAT029000
$2
bisacsh
072
7
$a
PBT
$2
thema
082
0 4
$a
519.535
$2
23
090
$a
QA278
$b
.F979 2018
100
1
$a
Funatogawa, Ikuko.
$3
827633
245
1 0
$a
Longitudinal data analysis
$h
[electronic resource] :
$b
autoregressive linear mixed effects models /
$c
by Ikuko Funatogawa, Takashi Funatogawa.
260
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2018.
300
$a
x, 141 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
SpringerBriefs in statistics,
$x
2191-544X
505
0
$a
Chapter 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.
520
$a
This 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.
650
0
$a
Longitudinal method.
$3
191626
650
1 4
$a
Statistical Theory and Methods.
$3
274054
650
2 4
$a
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
348605
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
275710
700
1
$a
Funatogawa, Takashi.
$3
827634
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
SpringerBriefs in statistics.
$3
557771
856
4 0
$u
https://doi.org/10.1007/978-981-10-0077-5
950
$a
Mathematics and Statistics (Springer-11649)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000164020
電子館藏
1圖書
電子書
EB QA278 F979 2018 2018
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-981-10-0077-5
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入