Abstract
Abstract There are growing demands to use prior and sample information for parameter estimation of a regression model in order to maintain consistency with underlying theory. To meet such demands, this paper provides an inequality constrained least-squares (ICLS) estimation, specifies an untruncated variance-covariance matrix of the ICLS estimates, and discusses their statistical properties in large-and small-sample cases. Finally, the ICLS and the ordinary least-squares OLS estimates are compared in terms of sample bias, sample mean-square error MSE and sample variance of the estimates by a Monte Carlo study.
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.
