Accession Number : ADA256891


Title :   A Bias Bound for Least Squares Linear Regression


Corporate Author : RAND CORP SANTA MONICA CA


Personal Author(s) : Duan, Naihua ; Li, Ker-Chau


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a256891.pdf


Report Date : Jan 1991


Pagination or Media Count : 13


Abstract : Least squares linear regression is one of the most widely statistical tools. It is based on a certain standard linear model where y denotes a scalar outcome variable, and x denotes a p-dimensional column vector of regressor variables. In empirical applications, it is unlikely for the standard linear model to hold exactly. Therefore we need to be concerned about possible violations of the model assumptions. For example, we might consider distribution violation: the error distribution might not be normal. There is a rich literature on robust methods for estimating the linear model in the presence of distribution violation.


Descriptors :   *LEAST SQUARES METHOD , *LINEAR REGRESSION ANALYSIS , *BIAS , VARIABLES , NONLINEAR ANALYSIS


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE