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saeMSPE (version 1.4)

Computing MSPE Estimates in Small Area Estimation

Description

Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation.

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Version

Install

install.packages('saeMSPE')

Monthly Downloads

229

Version

1.4

License

GPL (>= 2)

Maintainer

Peiwen Xiao

Last Published

November 25th, 2024

Functions in saeMSPE (1.4)

mspeNERpb

Compute MSPE through parameter bootstrap method for Nested error regression model
mspeFHjack

Compute MSPE through Jackknife-based MSPE estimation method for Fay Herriot model
mspeNERsumca

Compute MSPE through Sumca method for Nested error regression model
mspeFHsumca

Compute MSPE through Sumca method for Fay Herriot model
mspeNERdb

Compute MSPE through double bootstrap(DB) method for Nested error regression model
mspeNERlin

Compute MSPE through linearization method for Nested error regression model
mspeNERjack

Compute MSPE through Jackknife-based MSPE estimation method for Nested error regression model
mspeFHpb

Compute MSPE through parameter bootstrap method for Fay Herriot model
mspeFHlin

Compute MSPE through linearization method for Fay Herriot model
mspeFHdb

Compute MSPE through double bootstrap method for Fay Herriot model
saeMSPE-package

Compute MSPE Estimates for the Fay Herriot Model and Nested Error Regression Model
varfh

Estimates of the variance component using several methods for Fay Herriot model
varner

Estimates of the variance component using several methods for Nested error regression model.
wheatarea

Wheat area measurement and satellite data.