In many regression problems, data on the dependent variable are censored; that is, the values of some observations are known only to be above or else below some value. Such data often arise in accelerated life testing where life is the dependent variable and temperature or stress is the independent variable, and some test units have not failed at the time of the analysis. In such situations, the standard techniques of least squares estimation for the parameters of a linear regression model cannot be used, since the values of the censored observations are not known.