mspeFHdb: Compute MSPE through double bootstrap method for Fay Herriot...

In saeMSPE: Computing MSPE Estimates in Small Area Estimation

Compute MSPE through double bootstrap method for Fay Herriot model

Description

This function returns MSPE estimate with double bootstrap appoximation method for Fay Herriot model.

Usage

mspeFHdb(formula, data, D, K = 50, C = 50, method = 1, na_rm, na_omit)

Arguments

formula	(formula). Stands for the model formula that specifies the auxiliary variables to be used in the regression model. This should follow the R model formula syntax.
data	(data frame). It represents the data containing the response values and auxiliary variables for the Nested Error Regression Model.
D	(vector). It represents the knowing sampling variance for Fay Herriot model.
K	(integer). It represents the first bootstrap sample number. Default value is 50.
C	(integer). It represents the second bootstrap sample number. Default value is 50.
method	It represents the variance component estimation method. See "Details".
na_rm	A logical value indicating whether to remove missing values (NaN) from the input matrices and vectors. If TRUE, missing values in the input data (X, Y, and D) are automatically cleaned using internal functions. If FALSE, missing values are not removed. Defaults to FALSE.
na_omit	A logical value indicating whether to stop the execution if missing values (NaN) are present in the input data. If TRUE, the function will check for missing values in X, Y, and D. If any missing values are found, an error message will be raised, prompting the user to handle the missing data before proceeding. Defaults to FALSE.

Details

This method was proposed by P. Hall and T. Maiti. Double bootstrap method uses boostrap tool twice for Fay Herriot model to avoid the unattractivitive bias correction: one is to estimate the estimator bias, the other is to correct for bias.

Default value for method is 1, method = 1 represents the MOM method, method = 2 and method = 3 represents ML and REML method, respectively.

Value

A list with components:

MSPE	(vector) MSPE estimate based on double bootstrap method.
bhat	(vector) estimate of the unknown regression coefficients.
Ahat	(numeric) estimate of the variance component.

Author(s)

Peiwen Xiao, Xiaohui Liu, Yu Zhang, Yuzi Liu, Jiming Jiang

References

P. Hall and T. Maiti. On parametric bootstrap methods for small area prediction. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2006.

Examples

X <- matrix(runif(10 * 3), 10, 3)
X[,1] <- rep(1, 10) 
D <- (1:10) / 10 + 0.5
Y <- X %*% c(0.5, 1, 1.5) + rnorm(10, 0, sqrt(2)) + rnorm(10, 0, sqrt(D))
data <- data.frame(Y = Y, X1 = X[,2], X2 = X[,3])
formula <- Y ~ X1 + X2
result <- mspeFHdb(formula, data, D, K = 10, C = 10, method = 1)

saeMSPE documentation built on April 4, 2025, 5:18 a.m.