BLE_Categorical {BayesSampling}

Bayes Linear Method for Categorical Data

Description

Creates the Bayes Linear Estimator for Categorical Data

Usage

BLE_Categorical(ys, n, N, m = NULL, rho = NULL)

Arguments

ys

k-vector of sample proportion for each category.

n

sample size.

N

total size of the population.

m

k-vector with the prior proportion of each strata. If NULL, sample proportion for each strata will be used (non-informative prior).

rho

matrix with the prior correlation coefficients between two different units within categories. It must be a symmetric square matrix of dimension k (or k-1). If NULL, non-informative prior will be used.

Value

A list containing the following components:

• est.prop - BLE for the sample proportion of each category

• Vest.prop - Variance associated with the above

• Vs.Matrix - Vs matrix, as defined by the BLE method (should be a positive-definite matrix)

• R.Matrix - R matrix, as defined by the BLE method (should be a positive-definite matrix)

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

# 2 categories
ys <- c(0.2614, 0.7386)
n <- 153
N <- 15288
m <- c(0.7, 0.3)
rho <- matrix(0.1, 1)

Estimator <- BLE_Categorical(ys,n,N,m,rho)
Estimator

ys <- c(0.2614, 0.7386)
n <- 153
N <- 15288
m <- c(0.7, 0.3)
rho <- matrix(0.5, 1)

Estimator <- BLE_Categorical(ys,n,N,m,rho)
Estimator


# 3 categories
ys <- c(0.2, 0.5, 0.3)
n <- 100
N <- 10000
m <- c(0.4, 0.1, 0.5)
mat <- c(0.4, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.6)
rho <- matrix(mat, 3, 3)