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Nonparametric maximum likelihood estimation of probability densities by penalty function methods
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Thompson, J. R. Demontricher, G. F. Tapia, R. A. |
| Copyright Year | 1974 |
| Description | When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates. |
| File Size | 2392909 |
| Page Count | 39 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19750022788 |
| Archival Resource Key | ark:/13960/t15n10c3x |
| Language | English |
| Publisher Date | 1974-08-01 |
| Access Restriction | Open |
| Subject Keyword | Statistics And Probability Histograms Hilbert Space Theorems Maximum Likelihood Estimates Probability Density Functions Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
