PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Asymptotic normality of the $L_k$-error of the Grenander estimator
Vladimir N. Kulikov and Hendrik P. Lopuhaa
(2004) Technical Report. EURANDOM, the Netherlands.

Abstract

We investigate the limit behavior of the $L_k$-distance between a decreasing density $f$ and its nonparametric maximum likelihood estimator $\hat f_n$ for $k\geq 1$. Due to the inconsistency of $\hat f_n$ at zero, the case $k=2.5$ turns out to be some kind of transition point. We extent asymptotic normality of the $L_1$-distance to the $L_k$-distance for $1\leq k<2.5$, and obtain the analogous limiting result for a modification of the $L_k$-distance for $k\geq 2.5$. Since the $L_1$-distance is the area between $f$ and $\hat f_n$, which is also the area between the inverse $g$ of $f$ and the more tractable inverse $U_n$ of $\hat f_n$, the problem can be reduced immediately to deriving asymptotic normality of the $L_1$-distance between $U_n$ and $g$. Although we loose this easy correspondence for $k>1$, we show that the $L_k$-distance between $f$ and $\hat f_n$ is asymptotically equivalent to the $L_k$-distance between $U_n$ and $g$.

EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:791
Deposited By:Vladimir Koulikov
Deposited On:30 December 2004