Mean-Squared errors of small area estimators under a multivariate linear model for repeated measures data

Sammanfattning

In this paper, we discuss the derivation of the first and second moments for the proposed small area estimators under a multivariate linear model for repeated measures data. The aim is to use these moments to estimate the mean-squared errors (MSE) for the predicted small area means as a measure of precision. At the first stage, we derive the MSE when the covariance matrices are known. At the second stage, a method based on parametric bootstrap is proposed for bias correction and for prediction error that reflects the uncertainty when the unknown covariance is replaced by its suitable estimator.

Nyckelord

Mean-squared errors; Multivariate linear model; Parametric bootstrap; Repeated measures data; Small area estimation

Publicerad i

Communications in Statistics - Theory and Methods
2019, Volym: 48, nummer: 8, sidor: 2060-2073 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/10.1080/03610926.2018.1444178

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/101015