# BLE_SRS: Simple Random Sample BLE In BayesSampling: Bayes Linear Estimators for Finite Population

## Description

Creates the Bayes Linear Estimator for the Simple Random Sampling design (without replacement)

## Usage

 `1` ```BLE_SRS(ys, N, m = NULL, v = NULL, sigma = NULL, n = NULL) ```

## Arguments

 `ys` vector of sample observations or sample mean (`sigma` and `n` parameters will be required in this case). `N` total size of the population. `m` prior mean. If `NULL`, sample mean will be used (non-informative prior). `v` prior variance of an element from the population (bigger than `sigma^2`). If `NULL`, it will tend to infinity (non-informative prior). `sigma` prior estimate of variability (standard deviation) within the population. If `NULL`, sample variance will be used. `n` sample size. Necessary only if `ys` represent sample mean (will not be used otherwise).

## Value

A list containing the following components:

• `est.beta` - BLE of Beta (BLE for every individual)

• `Vest.beta` - Variance associated with the above

• `est.mean` - BLE for 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

## References

GonÃ§alves, 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```ys <- c(5,6,8) N <- 5 m <- 6 v <- 5 sigma <- 1 Estimator <- BLE_SRS(ys, N, m, v, sigma) Estimator # Same example but informing sample mean and sample size instead of sample observations ys <- mean(c(5,6,8)) N <- 5 n <- 3 m <- 6 v <- 5 sigma <- 1 Estimator <- BLE_SRS(ys, N, m, v, sigma, n) Estimator ```

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