857 research outputs found
Semiparametric posterior limits
We review the Bayesian theory of semiparametric inference following Bickel
and Kleijn (2012) and Kleijn and Knapik (2013). After an overview of efficiency
in parametric and semiparametric estimation problems, we consider the
Bernstein-von Mises theorem (see, e.g., Le Cam and Yang (1990)) and generalize
it to (LAN) regular and (LAE) irregular semiparametric estimation problems. We
formulate a version of the semiparametric Bernstein-von Mises theorem that does
not depend on least-favourable submodels, thus bypassing the most restrictive
condition in the presentation of Bickel and Kleijn (2012). The results are
applied to the (regular) estimation of the linear coefficient in partial linear
regression (with a Gaussian nuisance prior) and of the kernel bandwidth in a
model of normal location mixtures (with a Dirichlet nuisance prior), as well as
the (irregular) estimation of the boundary of the support of a monotone family
of densities (with a Gaussian nuisance prior).Comment: 47 pp., 1 figure, submitted for publication. arXiv admin note:
substantial text overlap with arXiv:1007.017
New Entropy Estimator with an Application to Test of Normality
In the present paper we propose a new estimator of entropy based on smooth
estimators of quantile density. The consistency and asymptotic distribution of
the proposed estimates are obtained. As a consequence, a new test of normality
is proposed. A small power comparison is provided. A simulation study for the
comparison, in terms of mean squared error, of all estimators under study is
performed
A Numerical Study of Entropy and Residual Entropy Estimators Based on Smooth Density Estimators for Non-negative Random Variables
In this paper, we are interested in the entropy of a non-negative random variable. Since the underlying probability density function is unknown, we propose the use of Poisson smoothed histogram density estimator in order to estimate the entropy. To study the performance of our estimator, we run simulations on a wide range of densities and compare our entropy estimators with the existing estimators that based on different approaches such as spacing estimators. Furthermore, we extend our study to residual entropy estimators which is the entropy of a random variable given that it has been survived up to time t
On An Entropy Estimator Based On a Non-parametric Density Estimator For Non-negative Data
In the recent decades, entropy has become more and more essential in statistics and machine learning. It features in many applications involving data transmission, cryptography, signal processing, network theory, bio-informatics, and so on. A large number of estimators for entropy have been proposed in the past ten years. Here we focus on entropy estimation for non-negative random variables. Specifically, the use of entropy estimator based on Poisson-weights density estimator is found to be of interest. We establish some asymptotic properties of the resulting estimators and present a simulation study comparing these with well known estimators in literature
Uniform-in-bandwidth consistency for kernel-type estimators of Shannon's entropy
We establish uniform-in-bandwidth consistency for kernel-type estimators of
the differential entropy. We consider two kernel-type estimators of Shannon's
entropy. As a consequence, an asymptotic 100% confidence interval of entropy is
provided
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