Estimation for parameters of interest in random effects growth curve models

Ip, W. H.; Wu, Mixia; Wang, Song-Gui; Wong, Hie Hua

doi:10.1016/j.jmva.2006.04.001

Cited by 5 publications

(6 citation statements)

References 9 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some references to this model, as well as more general models (see Chinchilli and Elswick 1985;Verbyla and Venables 1988;von Rosen 1989;and Bai and Shi 2007). If B 1 C 1 ¼ 0 and WF ¼ 0 we say that we have the growth curve model with random effects (see Ip et al 2007) whereas if only WF ¼ 0 holds (see Yokoyama and Fujikoshi 1992;Yokoyama 1995) where similar models are considered and where references to earlier works can be found. However, usually in these works one puts structures on the covariance matrices which will not be discussed in this article.…”

Section: Detailed Model Specificationmentioning

confidence: 99%

Small area estimation using reduced rank regression models

Rosen

2019

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

show abstract

Section: Detailed Model Specificationmentioning

confidence: 99%

Small area estimation using reduced rank regression models

Rosen

2019

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

show abstract

“…When F = 0 and UZ = 0 , then we have the growth curve model with background information, i.e., a mixture of GMANOVA and MANOVA models; some references to this model, as well as more general models, are Chinchilli and Elswick (1985), Verbyla andVenables (1988), von Rosen (1989) and Bai and Shi (2007). If B 2 C 2 = 0 and F = 0 , then we have the growth curve model with random effects (see Ip et al 2007) whereas if only F = 0 holds we refer to Yokoyama and Fujikoshi (1992) and Yokoyama (1995) where similar models are considered and where references to earlier works can be found. In these works, one puts structures on the covariance matrix which leads to somewhat different models than in this article.…”

Section: Definition 1 Letmentioning

confidence: 99%

Bilinear regression with random effects and reduced rank restrictions

Rosen

2019

Jpn J Stat Data Sci

View full text Add to dashboard Cite

Bilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. In the literature, bilinear models with random effects and bilinear models with latent variables have been discussed but there are no results available when combining random effects and latent variables. It is shown, via appropriate vector space decompositions, how to remove the random effects so that a well-known model comprising only fixed effects and latent variables is obtained. The spaces are chosen so that the likelihood function can be factored in a convenient and interpretable way. To obtain explicit estimators, an important standardization constraint on the random effects is assumed to hold. A theorem is presented where a complete solution to the estimation problem is given.

show abstract

“…This class of model has been considered by many authors and estimation of parameters of interest has been discussed with different choices of C and Z. See for example Nummi (1997); Ip et al (2007); Lange and Laird (1989); Yokoyama and Fujikoshi (1993); Yokoyama (2001); Srivastava and Singull (2012) for more details.…”

Section: Random Effect Growth Curve Modelmentioning

confidence: 99%

Small Area Estimation for Multivariate Repeated Measures Data

Ngaruye

2014

View full text Add to dashboard Cite

This thesis considers Small Area Estimation with a main focus on estimation and prediction theory for repeated measures data. The demand for small area statistics is for both cross-sectional and repeated measures data. For instance, small area estimates for repeated measures data may be used by public policy makers for different purposes such as funds allocation, new educational or health programs and in some cases, they might be interested in a given group of population.It has been shown that the multivariate approach for model-based methods in small area estimation may achieve substantial improvement over the usual univariate approach. In this work, we consider repeated surveys including the same subjects at different time points. The population from which a sample has been drawn is partitioned into several subpopulations and within all subpopulations there is the same number of group units. For this setting a multivariate linear regression model is formulated. The aim of the proposed model is to borrow strength across small areas and over time with a particular interest of growth profiles over time. The model accounts for repeated surveys, group individuals and random effects variations.The estimation of model parameters is discussed with a restricted maximum likelihood based approach. The prediction of random effects and the prediction of small area means across time points, per group units and for all time points are derived. The theoretical results have also been supported by a simulation study and finally, suggestions for future research are presented.v Populärvetenskaplig sammanfattning I den här avhandling diskuteras small area estimation (SAE) med fokus pȧ skattningar av parametrarna samt prediktion för slumpvariabler givet en modell för upprepade mät-ningar. Efterfrȧgan pȧ statistisk inferens för undergrupper av en population (small area statistics) har ökat markant. Till exempel kan sȧdan inferens för upprepad mätningar användas av offentliga beslutsfattare vid tilldelning av ekonomiska resurser, planering av nya utbildnings-eller hälsoprogram och i vissa fall kan det vara av intresse att studera en specifik undergrupp av befolkningen.Det har visat sig att den multivariata framställningen för modellbaserade metoder vid SAE kan uppnȧ betydande bättre resultat jämfört med det vanliga endimensionella modellantagandet. I detta arbete betraktar vi upprepade undersökningar pȧ samma individer vid olika tidpunkter. Populationen frȧn vilket ett urval har tagits är uppdelat i flera delpopulationer och inom alla undergrupper finns det samma antal observationer. För dessa förutsättningar, är en multivariat linjär regressionsmodell formulerad. Syftet med den för-slagna modellen är att lȧna egenskaper mellan undergrupperna och över tid. I modellen ingȧr det upprepade undersökningar, grupper av individer samt slumpmässiga effekter.Parametrarna i den föreslagna modellen skattas med hjälp av restricted maximum likelihood metoden. Prediktion av slumpmässiga effekter samt prediktion av de förväntade värdena i u...

show abstract

Estimation for parameters of interest in random effects growth curve models

Cited by 5 publications

References 9 publications

Small area estimation using reduced rank regression models

Small area estimation using reduced rank regression models

Bilinear regression with random effects and reduced rank restrictions

Small Area Estimation for Multivariate Repeated Measures Data

Contact Info

Product

Resources

About