# cmseRHNERM: Conditional mean squared error estimation of the empirical... In rhnerm: Random Heteroscedastic Nested Error Regression

## Description

Calculates the conditional mean squared error estimates of the empirical Bayes estimators under random heteroscedastic nested error regression models based on the parametric bootstrap.

## Usage

 1 cmseRHNERM(y, X, ni, C, k=1, maxr=100, B=100)

## Arguments

 y N*1 vector of response values. X N*p matrix containing N*1 vector of 1 in the first column and vectors of covariates in the rest of columns. ni m*1 vector of sample sizes in each area. C m*p matrix of area-level covariates included in the area-level parameters. k area number in which the conditional mean squared error estimator is calculated. maxr maximum number of iteration for computing the maximum likelihood estimates. B number of bootstrap replicates.

## Value

conditional mean squared error estimate in the kth area.

## Author(s)

Shonosuke Sugasawa

## References

Kubokawa, K., Sugasawa, S., Ghosh, M. and Chaudhuri, S. (2016). Prediction in Heteroscedastic nested error regression models with random dispersions. Statistica Sinica, 26, 465-492.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 #generate data set.seed(1234) beta=c(1,1); la=1; tau=c(8,4) m=20; ni=rep(3,m); N=sum(ni) X=cbind(rep(1,N),rnorm(N)) mu=beta[1]+beta[2]*X[,2] sig=1/rgamma(m,tau[1]/2,tau[2]/2); v=rnorm(m,0,sqrt(la*sig)) y=c() cum=c(0,cumsum(ni)) for(i in 1:m){ term=(cum[i]+1):cum[i+1] y[term]=mu[term]+v[i]+rnorm(ni[i],0,sqrt(sig[i])) } #fit the random heteroscedastic nested error regression C=cbind(rep(1,m),rnorm(m)) cmse=cmseRHNERM(y,X,ni,C,B=10) cmse

rhnerm documentation built on May 29, 2017, 12:41 p.m.