Robust and Partially Adaptive Estimation of Regression Models

It is well known that least squares estimates can be very sensitive to departures from normality. Various robust estimators, such as least absolute deviations, L(superscript "p") estimators or M-estimators provide possible alternatives to least squares when such departures occur. This paper applies a partially adaptive technique to estimate the parameters of William F. Sharpe's market model. This methodology is based on a generalized t-distribution and includes as special cases least squares, least absolute deviation, and L(superscript "p"), as well as some estimation procedures that have bounded and redescending influence functions. Coauthors are James B.McDonald, Ray D. Nelson, and Steven B. White. Copyright 1990 by MIT Press.