Premium
REGRESSION METHODOLOGY WITH GROSS OBSERVATION ERRORS IN THE EXPLANATORY VARIABLES * **
Author(s) -
Brecht H. David
Publication year - 1976
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.1540-5915.1976.tb00657.x
Subject(s) - estimator , econometrics , linear regression , statistics , robustness (evolution) , regression , mathematics , regression analysis , regression diagnostic , robust regression , local regression , seemingly unrelated regressions , least squares function approximation , computer science , polynomial regression , biochemistry , chemistry , gene
The robustness of linear programming regression estimators is examined where the disturbance terms are normally distributed and there are observation errors in the explanatory variables. These errors are occasional gross biases between one set of observations and another. The simulation of short series data offers preliminary evidence that when these biases have a non‐zero mean, MSAE estimation is more robust than least squares.