MSPE_McSpline: McSpline MSPE estimator

In rohosen/OBPSAE: Observed best predictors for small area estimation

Description

This function computes the McSPline MSPE estimator of the Observed Best Predictor (OBP).

Usage

MSPE_McSpline(formula, data, errorvar, A.BPE, K = 1000)

Arguments

formula	an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The variables included in formula must have a length equal to the number of small areas. More about the model specification are given under Details.
data	optional data frame containing the variable names in formula.
errorvar	vector containing the variances of the random errors for all small areas.
A.BPE	optional BPE estimate of random effects variance.
K	number of Monte Carlo simulations. Default is 1000.

Details

formula is specified in the form response ~ predictor where the predictor is univariate. formula has an implied intercept term. To remove the intercept term, use either y ~ x - 1 or y ~ 0 + x.

If A.BPE is missing, the function computes the BPE from data. User can also provide the true A instead if that is known.

Value

The function will return a list with the following object.

McSpline

McSpline estimator of the MSPE of OBP.

References

Bandyopadhyay R, Jiang J (2017) "Benchmarking the Observed Best Predictor"

Jiang J, Nguyen T, and Rao J. S. (2011), "Best Predictive Small Area Estimation", Journal of the American Statistical Association.

rohosen/OBPSAE documentation built on May 17, 2019, 2:22 p.m.