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Least Absolute Value Regression: Necessary Sample Sizes to Use Normal Theory Inference Procedures *
Author(s) -
Dielman Terry,
Pfaffenberger Roger
Publication year - 1988
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.1540-5915.1988.tb00298.x
Subject(s) - outlier , statistics , monte carlo method , inference , sample (material) , sample size determination , range (aeronautics) , econometrics , linear regression , extreme value theory , mathematics , regression analysis , computer science , artificial intelligence , chemistry , materials science , chromatography , composite material
Recently developed large sample inference procedures for least absolute value (LAV) regression are examined via Monte Carlo simulation to determine when sample sizes are large enough for the procedures to work effectively. A variety of different experimental settings were created by varying the disturbance distribution, the number of explanatory variables and the way the explanatory variables were generated. Necessary sample sizes range from as small as 20 when disturbances are normal to as large as 200 in extreme outlier‐producing distributions.