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saebnocov (version 0.1.0)

Small Area Estimation using Empirical Bayes without Auxiliary Variable

Description

Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) and Kleinman (1973) .

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Version

Install

install.packages('saebnocov')

Monthly Downloads

192

Version

0.1.0

License

GPL (>= 3)

Maintainer

Siti Rafika Fiandasari

Last Published

September 5th, 2022

Functions in saebnocov (0.1.0)

vectorRao

Vector g in Newton Raphson Method by J.N.K.Rao
pcapdir

Weighted Sample Mean and Variance
vectorClaire

Vector g in Newton Raphson Method by Claire E.B.O.
momentRao

Estimates alpha and beta parameter with Moment method by J.N.K.Rao
momentClaire

Estimates alpha and beta parameter with Moment method by Claire E.B.O.
alphabetaEB

Estimates alpha and beta parameter to obtain EB estimator
jackknifeEB

Small Area Estimation method with Empirical Bayes and its RRMSE value by Jackknife Method
matrixClaire

Matrix G in Newton Raphson method by Claire E.B.O.
matrixRao

Matrix G in Newton Raphson method by J.N.K.Rao
EBnaive

Small Area Estimation method with Empirical Bayes and its RRMSE value by Naive Method
bootstrapEB

Small Area Estimation method with Empirical Bayes and its RRMSE value by Bootstrap Method
estEBnaive

Small Area Estimation method with Empirical Bayes and its RRMSE value by Naive Method
dataEB

Sample Data for Practice
newtonRaphsonC

Estimates alpha and beta parameter with Newton Raphson method by Claire E.B.O.
newtonRaphsonR

Estimates alpha and beta parameter with Newton Raphson method by J.N.K. Rao