ROBUST REGRESSION WITH M, S, AND LTS ESTIMATES IN THE CASE OF SHARIA STOCKS IN INDONESIA

Reviana Astuti, NIM.: 18106010012 (2022) ROBUST REGRESSION WITH M, S, AND LTS ESTIMATES IN THE CASE OF SHARIA STOCKS IN INDONESIA. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Regression analysis is one of the statistical methods used to determine the effect of the dependent variable on the independent variable. The purpose of the regression analysis is to obtain a regression parameter estimation model of a data. To estimate the regression parameters, the Least Squares Method (MKT) or commonly known as Ordinary Least Square (OLS) can be used. However the OLS method is not appropriate for data containing outliers or outliers. Thus, the robust regression estimation method is used. In this research the robust regression model used is the Least Trimmed Square estimation (LTS), Scale estimation (S), and Maximum Likelihood Type estimation (M). Research purposes this is to obtain the best model in forming the parameter estimation model regression by comparing the three robust regression models on the data. Estimation the best robust regression is the estimation with the Residual Standard Error (RSE) value and the Smallest Mean Squared Error (MSE) and the largest Adjusted R-square. Studies the case in this study is Indonesian sharia shares registered with ISSI period January 2017 - December 2021 with the dependent variable ISSI stock index and the independent variable inflation, Bank Indonesia Syari’ah Certificates (SBIS), and Gross Domestic Product (GDP). The results of the case study research on the closing price of Indonesian syari’ah ham registered with ISSI for the period January 2017 - December 2021 it is found that the best estimate is the S-estimation with a value of ¯R2 of 0.5482, the RSE value is 7.899 and the MSE value is 176.5142.

Item Type: Thesis (Skripsi)
Additional Information: Pembimbing: Mohammad Farhan Qudratullah, S.Si., M.Si.
Uncontrolled Keywords: Robust Regression Analysis; Robust Regression Estimation; Outliers
Subjects: Matematika
Divisions: Fakultas Sains dan Teknologi > Matematika (S1)
Depositing User: Muchti Nurhidaya [muchti.nurhidaya@uin-suka.ac.id]
Date Deposited: 13 Feb 2023 09:17
Last Modified: 13 Feb 2023 09:17
URI: http://digilib.uin-suka.ac.id/id/eprint/56130

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