Simultaneous confidence band for single-index random effects models with longitudinal data

2014 ◽  
Vol 85 ◽  
pp. 6-14 ◽  
Author(s):  
Suigen Yang ◽  
Liugen Xue ◽  
Gaorong Li
Keyword(s):  
Longitudinal Data ◽  
Random Effects ◽  
Confidence Band ◽  
Random Effects Models ◽  
Single Index ◽  
Simultaneous Confidence Band

Estimating Heterogeneity in Random Effects Models for Longitudinal Data

2005 ◽  
Vol 47 (3) ◽  
pp. 329-345 ◽  
Author(s):  
A. Lemenuel-Diot ◽  
A. Mallet ◽  
C. Laveille ◽  
R. Bruno
Keyword(s):  
Longitudinal Data ◽  
Random Effects ◽  
Random Effects Models

Random-Effects Models for Longitudinal Data Using Gibbs Sampling

Biometrics ◽  
1993 ◽  
Vol 49 (2) ◽  
pp. 441 ◽  
Author(s):  
W. R. Gilks ◽  
C. C. Wang ◽  
B. Yvonnet ◽  
P. Coursaget
Keyword(s):  
Longitudinal Data ◽  
Gibbs Sampling ◽  
Random Effects ◽  
Random Effects Models

Random-Effects Models for Longitudinal Data

Biometrics ◽  
1982 ◽  
Vol 38 (4) ◽  
pp. 963 ◽  
Author(s):  
Nan M. Laird ◽  
James H. Ware
Keyword(s):  
Longitudinal Data ◽  
Random Effects ◽  
Random Effects Models

scholarly journals Random Effects Models for Longitudinal Data

2010 ◽  
pp. 37-96 ◽  
Author(s):  
Geert Verbeke ◽  
Geert Molenberghs ◽  
Dimitris Rizopoulos
Keyword(s):  
Longitudinal Data ◽  
Random Effects ◽  
Random Effects Models

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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