On the Estimation of a Probability Density Function by the Maximum Penalized Likelihood Method
The Annals of Statistics · 1982 · Volume 10 · Issue 3
Abstract
A class of probability density estimates can be obtained by penalizing the likelihood by a functional which depends on the roughness of the logarithm of the density. The limiting case of the estimates as the amount of smoothing increases has a natural form which makes the method attractive for data analysis and which provides a rationale for a particular choice of roughness penalty. The estimates are shown to be the solution of an unconstrained convex optimization problem, and mild natural conditions are given for them to exist. Rates of consistency in various norms and conditions for asymptotic normality and approximation by a Gaussian process are given, thus breaking new ground in the theory of maximum penalized likelihood density estimation.
Keywords
46E35, 62E20, 62G05, 65D10, asymptotic normality, consitency, convex optimization existence and uniqueness, data analysis, Gaussian process, penalized likelihood, Probability density estimate, rates, reproducing kernel Hilbert space, roughness penalty, smoothing, Sobolev space, strong approximation
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