Eksponensial
Vol 8 No 1 (2017)

Perbandingan Metode Bootstrap Dan Jackknife Resampling Dalam Menentukan Nilai Estimasi Dan Interval Konfidensi Parameter Regresi

Dessy Ariani (Mahasiswa Program Studi Statistika FMIPA Universitas Mulawarman)
Yuki Novia Nasution (Dosen Program Studi Statistika FMIPA Universitas Mulawarman)
Desi Yuniarti (Dosen Program Studi Statistika FMIPA Universitas Mulawarman)



Article Info

Publish Date
21 Dec 2017

Abstract

Regression analysis is a study that describes and evaluates the relationship between an independent variable and the dependent variable for the purpose of estimating or predicting the value of the dependent variable based on the value of the independent variables. Resampling is used when samples obtained for analyzing is less. In this study, Bootstrap method and Jackknife method are using. Both methods are used to find the value of regression parameter estimates and confidence intervals of regression parameter values which applied to the data position of Public Deposits in four groups of banks : Persero Banks, Government Banks, National Private Banks and Foreign Banks to knowing the best resampling methods to find the value of regression parameter estimates and confidence intervals of regression parameter values. There are three independent variables which are used in this study, namely investments loans, working capital loans and consumer loans. From the research results, it is obtained that the Jackknife method is the most appropriate method because it has smaller standard error values so Jackknife methods have a narrow range confidence intervals.

Copyrights © 2017






Journal Info

Abbrev

exponensial

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

Description

Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its ...