Perbandingan Penduga M, S, dan MM pada Regresi Linier dalam Menangani Keberadaan Outlier

Lainun, Hanifah; Tinungki, Georgina Maria; Amran, Amran

doi:10.20956/jmsk.v15i1.4427

Cited by 3 publications

(8 citation statements)

References 2 publications

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“…The classical assumption tests carried out in this study included the normality error test (ɛ), multicollinearity test, autocorrelation test, and heteroscedasticity test. A total of 9 observation objects were considered outliers and were discarded in order to pass the classical assumption test, leaving 87 objects to be analyzed in the data processing (Lainun, Tinungki, & Amran, 2018;Tinungki, 2019).…”

Section: Methodsmentioning

confidence: 99%

Determinants of Dividend Policy of Real Estate, Property, and Building Construction Companies Listed in IDX with Unbalanced Panel Data Approach

Hartono

Matusin

2020

TIJAB

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This study involved companies engaging in real estate, property, and building construction companies listed in IDX for 2013 - 2019 period as the population. Unbalanced panel data regression was employed using the SPSS version 22 and E-Views version 10 to analyze the data and to test the hypotheses. The results showed that the previous year's dividend had a positive effect on dividend policy, while company size had a negative effect on dividend policy. This study proved that previous year's dividend and company size were key variables that determined companies’ dividend policy and they were major investment considerations for investors in order to obtain optimal returns.

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Section: Methodsmentioning

confidence: 99%

Determinants of Dividend Policy of Real Estate, Property, and Building Construction Companies Listed in IDX with Unbalanced Panel Data Approach

Hartono

Matusin

2020

TIJAB

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“…In this research, weighting Tukey's Bisquare was chosen because weighting Tukey's Bisquare using a tunning constant (c) of up to 4.568 could achieve 95% efficiency. As the explanation of previous research, the use of c = 4.568 will make for 95% efficiency (Lainun and Tinungki, 2018).…”

Section: Discussion the Analysis Of M Estimationmentioning

confidence: 89%

“…The data outlier will make the analysis of OLS regression become refracted on the interpretation results and inefficient (Herawati, Nisa, and Setiawan, 2011). This is because the smallest quadrate is sensitive to the outlier (Lainun and Tinungki, 2018).…”

Section: Discussion the Analysis Of M Estimationmentioning

confidence: 99%

“…The higher efficiency of an estimator, the lower its breakdown point value. The lower breakdown point value of an estimator, the lower its ability in resolving outlier (Lainun and Tinungki, 2018). From the identification of the outlier result, 80% of the research data included outliers.…”

Section: Discussion the Analysis Of M Estimationmentioning

confidence: 99%

“…If the data are abnormal, it could have been influenced by the condition of data outlier (Herawati et al, 2011). The OLS method has the weakness of being sensitive towards the data outlier (Lainun and Tinungki, 2018).…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of M-Estimation and S-Estimation on the Factors Affecting Ir DHF in East Java in 2017

Mardiana¹,

Wibowo

Mahmudah

et al. 2021

ijph

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Robust regression on M estimation and S estimation is the Ordinary Least Square (OLS) regression on the data outlier. East Java is one of the provinces in Indonesia with a high case fatalitiy rate (1.34%). The raising of Dengue Haemoragic Fever (DHF) in East Java has been influenced by climate, population density, human behavior, and environmental sanitation. This study aimed to compare robust regression research by using M estimation and S estimation on the factors that affect IR DHF. This was done to get the best model regression on the data outlier based on the biggest R2 adjusted and the smallest MSE. This study utitlized observational research with a non-reactive research design using secondary data. The independent variable consisted of population density, healthy behavior, healthy living environment house, and precipitation in East Java in 2017. The dependent variable was incident rate of DHF in 2017. The population included 38 regencies in East Java, while the sample was 35 regencies/cities selected using simple random sampling. The analysis used robust regression on M estimation and S estimation weighting by Tukey’s Bisquare. Robust regression on S estimation was found to be the best robust regression on data outlier with R2 adjusted (0.996) and MSE (0.229). Robust regression on S estimation was = 54.826 + 0.011 (population density) – 0.136 (% healthy behavior) - 0,404 (% healthy house ) - 0,005 (precipitation).Some factors that affect IR DHF can be the main focus for the prevention and control of DHF for the government and society. Keywords: robust regression, outlier, estimation, estimation, DHF

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Implementasi Regresi Robust untuk Mengetahui Faktor-Faktor yang Mempengaruhi Produksi Padi di Indonesia

Rasantaka

Ashshidiqi

Yulianti

et al. 2022

JSA

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Seiring dengan pertambahan jumlah penduduk tentu akan membutuhkan ketersediaan bahan pangan pokok masyarakat yang semakin meningkat pula. Padi sebagai salah satu komoditas utama bahan pangan pokok masyarakat Indonesia tentu menjadi perhatian penting bagi pemerintah Indonesia. Menurut penilaian dari The Global Food Safety Initiative (GFSI) terjadi penurunan nilai indek ketahanan pangan di Indonesia sebesar 2.2% di tahun 2021. Hal ini diikuti dengan banyak dilakukannya eksploitasi terhadap lahan pertanian seperti di Jawa Tengah yang menjadi salah satu lumbung padi di Indonesia. Ketergantungan masyarakat Indonesia terhadap nasi sebagai makanan pokok membawa tantangan yang besar dalam mengelola ketahanan atau ketersediaan padi di Indonesia. Untuk itu dalam penelitian ini akan digunakan analisis regeresi robust dalam melakuakn analisis untuk mengetahui faktor-faktor yang mempengaruhi produksi padi di Indonesia berdasarkan literatur terdahulu yang menjadi landasan seperti luas panen, realisasi pupuk, rata-rata curah hujan, produksi benih padi, dan jumlah tenaga kerja sektor pertanian. Dari hasil analisis yang didapatkan diketahui bahwa yang berpengaruh signifikan terhadap produksi padi dari faktor yang ada yaitu luas panen dan produksi benih padi.

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Perbandingan Penduga M, S, dan MM pada Regresi Linier dalam Menangani Keberadaan Outlier

Cited by 3 publications

References 2 publications

Determinants of Dividend Policy of Real Estate, Property, and Building Construction Companies Listed in IDX with Unbalanced Panel Data Approach

Determinants of Dividend Policy of Real Estate, Property, and Building Construction Companies Listed in IDX with Unbalanced Panel Data Approach

A Comparison of M-Estimation and S-Estimation on the Factors Affecting Ir DHF in East Java in 2017

Implementasi Regresi Robust untuk Mengetahui Faktor-Faktor yang Mempengaruhi Produksi Padi di Indonesia

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