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Goal Programming Approach for Regression Median *
Author(s) -
Sueyoshi Toshiyuki,
Chang YihLong
Publication year - 1989
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1989.tb01414.x
Subject(s) - estimator , least absolute deviations , statistics , robust regression , monte carlo method , regression , computer science , regression analysis , data set , mean absolute percentage error , m estimator , mathematics , linear regression , mean squared error
This study presents a new robust estimation method that can produce a regression median hyper‐plane for any data set. The robust method starts with dual variables obtained by least absolute value estimation. It then utilizes two specially designed goal programming models to obtain regression median estimators that are less sensitive to a small sample size and a skewed error distribution than least absolute value estimators. The superiority of new robust estimators over least absolute value estimators is confirmed by two illustrative data sets and a Monte Carlo simulation study.