Asymptotic normality of randomly truncated stochastic algorithms
نویسنده
چکیده
منابع مشابه
Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data
Kernel density estimators are the basic tools for density estimation in non-parametric statistics. The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the location of the sample points. In this paper, we initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...
متن کاملRandomized-direction Stochastic Approximation Algorithms Using Deterministic Sequences
We study the convergence and asymptotic normality of a generalized form of stochastic approximation algorithm with deterministic perturbation sequences. Both one-simulation and two-simulation methods are considered. Assuming a special structure of deterministic sequence, we establish sufficient condition on the noise sequence for a.s. convergence of the algorithm. Construction of such a special...
متن کاملA Lynden-Bell integral estimator for extremes of randomly truncated data
This work deals with the estimation of the extreme value index and extreme quantiles for heavy tailed data, randomly right truncated by another heavy tailed variable. Under mild assumptions and the condition that the truncated variable is less heavy-tailed than the truncating variable, asymptotic normality is proved for both estimators. The proposed estimator of the extreme value index is an ad...
متن کاملOn Asymptotic Problems of Parameter Estimation in Stochastic Pde's: the Case of Discrete Time Sampling
The problem of estimating parameters of randomly perturbed PDE's is considered. ML estimators based on discrete time sampling of M observable Fourier coeecients of the random eld governed by the stochastic PDE in question are studied. Necessary and suucient conditions are given for the consistency, asymptotic normality and asymp-totic eeciency of the ML estimators when M ! 1. These conditions a...
متن کاملOnline estimation of the asymptotic variance for averaged stochastic gradient algorithms
Stochastic gradient algorithms are more and more studied since they can deal efficiently and online with large samples in high dimensional spaces. In this paper, we first establish a Central Limit Theorem for these estimates as well as for their averaged version in general Hilbert spaces. Moreover, since having the asymptotic normality of estimates is often unusable without an estimation of the...
متن کامل