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All Journal FORUM STATISTIKA DAN KOMPUTASI Jurnal Siger Matematika
Mustofa Usman
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Mathematics

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PERBANDINGAN KUASA UJI PENDEKATAN BIGGERS DAN SATTERTHWAITE-COCHRAN DALAM MENGANALISIS DATA HILANG PADA RANCANGAN KELOMPOK TERACAK LENGKAP Achmad Firdaus; Mustofa Usman; Netti Herawati
FORUM STATISTIKA DAN KOMPUTASI Vol. 8 No. 1 (2003)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (298.504 KB)

Abstract

This study was aimed to compare type I error in estimating missing data by Biggers and Satterhwaite-Cochran methods in  Randomized Comple Block Design.  Simulation study using Gauss software showed that type I error for Biggers approach are over estimate.  The problem  can be overcomed by Satterhwaite-Cochran approach.Keyword:  Missing data, randomized Block design, Biggers, Satterhwaite-Cochran.
Perbandingan Metode Bootstrap, Jacknife Jiang Dan Area Specific Jacknife Pada Pendugaan Mean Square Error Model Beta-Bernoulli Yesi Santika; Widiarti Widiarti; Fitriani Fitriani; Mustofa Usman
Jurnal Siger Matematika Vol 2, No 1 (2021): Jurnal Siger Matematika
Publisher : FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (487.04 KB) | DOI: 10.23960/jsm.v2i1.2756

Abstract

Small area estimation is defined as a statistical technique for estimating the parameters of a subpopulation with a small sample size. One method of estimating small area parameters is the Empirical Bayes (EB) method.  The accuracy of the Empirical Bayes (EB) estimator can be measured by evaluating the Mean Squared Error (MSE). In this study, 3 methods to determine MSE in the EB estimator of the Beta-Bernoulli model will be compared, namely the Bootstrap, Jackknife Jiang and Area-specific Jackknife methods.  The study is carried out theoretically and empirically through simulation with R-studio software version 1.2.5033. The simulation results in a number of areas and pairs of prior distribution parameter values, namely Beta, show the effect of sample size and parameter value pairs on the Mean Square Error (MSE) value. The larger the number of areas and the smaller the initial ????, the smaller the MSE value.  The area-specific Jackknife method produces the smallest MSE in the number of areas 100 and the Beta parameter value 0.1.
Co-Authors Achmad Firdaus Budi Ruswandi Fitriani Netti Herawati Warsono Warsono Widiarti Widiarti Yesi Santika