BLE_Reg: General BLE case

In BayesSampling: Bayes Linear Estimators for Finite Population

Description

Calculates the Bayes Linear Estimator for Regression models (general case)

Usage

BLE_Reg(ys, xs, a, R, Vs, x_nots, V_nots)

Arguments

ys	response variable of the sample
xs	explicative variable of the sample
a	vector of means from Beta
R	covariance matrix of Beta
Vs	covariance of sample errors
x_nots	values of X for the individuals not in the sample
V_nots	covariance matrix of the individuals not in the sample

Value

A list containing the following components:

• est.beta - BLE of Beta

• Vest.beta - Variance associated with the above

• est.mean - BLE of each individual not in the sample

• Vest.mean - Covariance matrix associated with the above

• est.tot - BLE for the total

• Vest.tot - Variance associated with the above

Source

https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886

References

Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014). Bayes Linear Estimation for Finite Population with emphasis on categorical data. Survey Methodology, 40, 15-28.

Examples

xs <- matrix(c(1,1,1,1,2,3,5,0),nrow=4,ncol=2)
ys <- c(12,17,28,2)
x_nots <- matrix(c(1,1,1,0,1,4),nrow=3,ncol=2)
a <- c(1.5,6)
R <- matrix(c(10,2,2,10),nrow=2,ncol=2)
Vs <- diag(c(1,1,1,1))
V_nots <- diag(c(1,1,1))

Estimator <- BLE_Reg(ys, xs, a, R, Vs, x_nots, V_nots)
Estimator

BayesSampling documentation built on May 2, 2021, 1:06 a.m.