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Dwi Ispriyansti
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ESIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN ITERASI HOERL, KENNARD, DAN BALDWIN (HKB) UNTUK PENANGANAN MULTIKOLINIERITAS Nur Aeniatus Solekakh; Dwi Ispriyansti; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1277.774 KB) | DOI: 10.14710/j.gauss.v4i4.17099

Abstract

Regression analysis is statistical method used to analyze the dependence of respond variables to predictor variable. In multiple linear regression analysis, there are assumptions that must be met, they are normality, homoscedasticity, absence of multicollinearity, and absence of autocorrelation. One of assumption frequently found is multicollinearity. If multicollineraity is exist between predictor variables, then regression analysis with ordinary least square is no longer used. Ridge regression is regression method to handle multicollinearity. The ridge estimator involves adding biasing constant (k) to each diagonal element of  X’X. Biasing constant (k) is determined by Hoerl, Kennard, and Baldwin (HKB) iteration method. This regression can be applied to inflation rate in Indonesia data and the factors that influence, they are BI rate, money supply, and exchange rate of rupiah. Ridge regression analysis, the VIF (Variance Inflation Factor) values for each predictor variables BI rate, money supply, and exchange rate of rupiah are 1.6637, 3.2712, and 4.3309. SinceVIF values are not exceed to 10, then there is no multicollinearity in ridge regression model.Keywords: Inflation,  Multikollinearity, Ridge Regression,  HKB Iteration, VIF
PEMODELAN REGRESI HECKIT UNTUK KONSUMSI SUSU DI PROVINSI JAWA TENGAH Dwi Asti Rakhmawati; Dwi Ispriyansti; Agus Rusgiyono
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.946 KB) | DOI: 10.14710/j.gauss.v6i3.19303

Abstract

In multiple regression if the response variable is dummy variable then it can not be used because it will produce biased and inconsistent estimator. The appropriate method for binary response variables is Heckit Regression. Estimation of Heckit Regression parameter using Two Step Method of Procurement is the selection equation and the result equation. In the selection equation will get new variable that is Invers Mills Ratio. While in Equation Result of new variable of Inverse Mills Ratio is added as independent variable along with other independent variable. Heckit Regression method is applied to household milk consume data obtained from 2015 SUSENAS results as many as 201 households. The response variable used is household expenditure for milk consumption. The independent variables used are the working status of the head of the household, the last education of the head of the household, the number of household members, the number of toddler age in the family and the income of the household.Keywords: Multiple Regression, OLS, Heckit Regression, Two Step Procedure, Milk consumption expenditure.
Co-Authors Agus Rusgiyono Dwi Asti Rakhmawati Nur Aeniatus Solekakh Sudarno Sudarno