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.
Volume (Year): 72 (1990)
Issue (Month): 2 (May)
|Contact details of provider:|| Web page: http://mitpress.mit.edu/journals/|
|Order Information:||Web: http://mitpress.mit.edu/journal-home.tcl?issn=00346535|
When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:72:y:1990:i:2:p:321-27. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Pollock-Nelson)
If references are entirely missing, you can add them using this form.