BLE_Reg {BayesSampling}

General BLE case

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