Censored Variables and Censored Regression
نویسنده
چکیده
A censored variable has a large fraction of observations at the minimum or maximum. Because the censored variable is not observed over its entire range ordinary estimates of the mean and variance of a censored variable will be biased. Ordinary least squares (OLS) estimates of its regression on a set of explanatory variables will also be biased. These estimates are not consistent, i.e., the bias does not become smaller when the sample size increases. This note explains how maximum likelihood estimates can be obtained using PRELIS 2.53. The maximum likelihood estimates are consistent, i.e., the bias is small in large samples.
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