Abstract
This paper introduces a semi-parametric method for estimating regression co underlying parent population of errors in censored. The method is an example of the method of sieves; and it provides simultaneous estimates of the regression coefficients and the density of the underlying parent population. In the very simplest terms, the underlying unknown density is approximated by a spline with mesh size approaching zero with the sample size. The values of the density at the knots are then added to the list of the usual unknown parameters in a censored regression model, e.g., the regression coefficients and scale parameter. A quasi-likelihood function using the approximate spline density is then maximized over all the parameters mentioned above. The method is shown to result in strongly consistent parameter estimates.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
