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Nusyirwan
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Journal : Sciencestatistics: Journal of Statistics, Probability, and Its Application

 1 
Perbandingan Pembobot Welsch dan Tukey Bisquare pada Regresi Robust S-estimator Nurhafifah, Fifi; Khoirin Nisa; Nusyirwan; Rizki Agung Wibowo
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 2 (2024): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i2.6156

Abstract

Regresi robust merupakan sebuah metode yang dikembangkan untuk memiliki kinerja yang baik ketika data yang dianalisis menyimpang dari asumsi yang mendasari, misalnya terdapat pencilan yang dapat menyebabkan galat menjadi tidak berdistribusi normal. Salah satu metode estimasi pada regresi robust adalah S-estimator, metode ini memiliki fungsi pembobot antara lain pembobot Welsch dan Tukey Bisquare. Pada penelitian ini, kami membandingkan bobot-bobot pada metode S-estimator pada data berukuran: 30, 60, 100 dan 200 yang diberikan kontaminasi pencilan sebesar: 5%, 10%, 15%, 20%, 25% dan 30%. Berdasarkan hasil simulasi diperoleh bahwa kedua pembobot menghasilkan nilai MSE (Mean Square Error) dan bias yang serupa. Sehingga dapat disimpulkan bahwa kedua pembobot memberikan hasil yang sesuai dan sama baiknya pada regresi S-estimator. Robust regression is a method developed to have good performance when the analyzed data deviates from the underlying assumptions, for example, there are outliers that can cause errors to be not normally distributed. One of the estimation methods in robust regression is the S-estimator, this method has weighting functions, including the Welsch and Tukey Bisquare weights. In this study, we compared the weights in the S-estimator method on data sizes: 30, 60, 100 and 200 which were given outlier contamination of: 5%, 10%, 15%, 20%, 25% and 30%. Based on the simulation results, it is found that the two weights produce similar MSE (Mean Square Error) and bias values. So it can be concluded that the two weights provide appropriate and equally good results in the S-estimator regression
Estimasi Model Fixed Effect Pada Analisis Regresi Data Panel Dengan Metode Least Square Dummy Variable (LSDV) Junia Rahma Nur Imani; Khoirin Nisa; Dorrah Aziz; Nusyirwan
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 3 No. 1 (2025): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v3i1.7525

Abstract

Data panel merupakan gabungan antara data cross section dan data time series. Salah satu model analisis regresi data panel adalah model fixed effect. Model fixed effect mempunyai asumsi bahw intersep berbeda untuk setiap individu, tetapi koefisien slope konstan. Estimasi dilakukan dengan menggunakan variabel dummy untuk menjelaskan adanya perbedaan intersep antar individu. Penelitian ini bertujuan untuk mengestimasi model fixed effect pada analisis regresi data panel dengan metode least square dummy variable dan menerapkannya pada data upah minimum provinsi di Indonesia tahun 2014-2017. Berdasarkan hasil penelitian yang telah dilakukan dengan menggunakan estimasi parameter = untuk model fixed effect pada analisis regresi data panel upah minimum provinsi di Indonesia diperoleh model sebagai berikut, = 5.248452+ + 0.007415 + 0.002882 + 1.63E-07dengan, = upah minimum provinsi, = indeks harga konsumen, = tingkat partisipasi angkatan kerja, = produk domestik regional bruto dan = variabel dummy, k = 1,2, ...,33 (provinsi). Panel data is a combination of cross section data and time series data. One of panel data regression analysis model is the fixed effect model. The fixed effect model has the assumption that intercepts are different for each individual, but the slope coefficient is constant. Estimation is done by using dummy variables to explain the existence of intercept differences between individuals. This study aims to estimate the fixed effect model in panel data regression analysis using the least square dummy variable method and apply it to the provincial minimum wage data in Indonesia in 2014-2017. Based on the results of the research that has been done by using paremeter estimator = for fixed effect model in the panel regression analysis on provincial minimum wage data in Indonesia, we obtained as follows, = 5.248452+ + 0.007415 + 0.002882 + 1.63E-07 with, = provincial minimum wage, = consumer price index, = labor force participation rate, = regional gross domestic product, = dummy variable , k = 1,2, ...,33 (province).
Co-Authors Adriansyah Dorrah Aziz Fajar Pradana Fharel Abdillah Gani Pratama Hidayat, Rahmadani Janwardi Reywanda Tarigan Junia Rahma Nur Imani Khoirin Nisa Muhammad Yusuf Nurhafifah, Fifi Rizki Agung Wibowo Ruzita Sumiati Widya Anggrainia Xhycho Vachanidyo Yuli Yetri Zulfadly Koto