Indonesian Journal of Applied Statistics
Vol 1, No 1 (2018)

A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia

Hasih Pratiwi (Statistics Study Program, Universitas Sebelas Maret)
Yuliana Susanti (Statistics Study Program, Universitas Sebelas Maret)
Sri Sulistijowati Handajani (Statistics Study Program, Universitas Sebelas Maret)

Article Info

Publish Date
19 Sep 2018

Abstract

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator

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Journal Info
Indonesian Journal of Applied Statistics
Website

Abbrev

ijas

Publisher

Universitas Sebelas Maret

Subject

Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science

Description

Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific ...