Improved $U$-tests for variance components in one-way random effects models

2020 ◽  
Vol 34 (3) ◽  
pp. 464-477
Author(s):  
Juvêncio Santos Nobre ◽  
Julio M. Singer ◽  
Maria J. Batista
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Random Effects Models

scholarly journals Effects of Cluster Sizes on Variance Components in Two-Stage Sampling

2015 ◽  
Vol 31 (4) ◽  
pp. 763-782
Author(s):  
Richard Valliant ◽  
Jill A. Dever ◽  
Frauke Kreuter
Keyword(s):  
Sample Size ◽  
Random Effects ◽  
Variance Components ◽  
Sample Size Calculation ◽  
Household Surveys ◽  
Sample Design ◽  
Two Stage ◽  
Random Effects Models ◽  
Standard Software ◽  
The U.S

Abstract Determining sample sizes in multistage samples requires variance components for each stage of selection. The relative sizes of the variance components in a cluster sample are dramatically affected by how much the clusters vary in size, by the type of sample design, and by the form of estimator used. Measures of the homogeneity of survey variables within clusters are related to the variance components and affect the numbers of sample units that should be selected at each stage to achieve the desired precision levels. Measures of homogeneity can be estimated using standard software for random-effects models but the model-based intracluster correlations may need to be transformed to be appropriate for use with the sample design. We illustrate these points and implications for sample size calculation for two-stage sample designs using a realistic population derived from household surveys and the decennial census in the U.S.

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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