Small area estimation under a multivariate linear model for repeated measures data

Sammanfattning

In this article, small area estimation under a multivariate linear model for repeated measures data is considered. The proposed model aims to get a model which borrows strength both across small areas and over time. The model accounts for repeated surveys, grouped response units, and random effects variations. Estimation of model parameters is discussed within a likelihood based approach. Prediction of random effects, small area means across time points, and per group units are derived. A parametric bootstrap method is proposed for estimating the mean squared error of the predicted small area means. Results are supported by a simulation study.

Nyckelord

Maximum likelihood; multivariate linear model; prediction of random effects; repeated measures data; small area estimation; 62F10; 62H12; 62D05

Publicerad i

Communications in Statistics - Theory and Methods
2017, volym: 46, nummer: 21, sidor: 10835-10850
Utgivare: TAYLOR & FRANCIS INC

SLU författare

• von Rosen, Dietrich

  • Institutionen för energi och teknik, Sveriges lantbruksuniversitet
    
  • Linköpings universitet

UKÄ forskningsämne

Sannolikhetsteori och statistik

Publikationens identifierare

• DOI: https://doi.org/10.1080/03610926.2016.1248786

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/92641