A Bias Bound for Least Squares Linear Regression
RAND CORP SANTA MONICA CA
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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.
- Statistics and Probability