Abstract
The present paper is concerned with the recent developments in nonparametric estimation of probability density. Two methods for the estimation of probability densities from finite samples of independent identical distributed random variables are discussed. First, the spatial filters technique or kernel estimators are reviewed. Second, the method of maximum penalized likelihood estimators of probability density functions is reviewed. More specifically, some of the very recent theoretical results on this subject by the authors are discussed. An application of the kernel estimation approach to nonparametric regression analysis is presented.
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.
