Due to its conceptual simplicity and its proven effectiveness in real-time detection and removal of radio frequency interference (RFI) from radio astronomy data, the spectral kurtosis (SK) estimator is likely to become a standard tool of a new generation of radio telescopes. However, the SK estimator in its original form must be developed from instantaneous power spectral density estimates, and...

In this paper, we propose a new robust estimator for regression problems in the form of the linear combination of quantile regressions. The proposed robust regression estimator is helpful for the conditional mean estimation especially when the error distribution is asymmetric or/and heteroscedastic, where conventional robust regressions yield considerable bias to the conditional mean. First we ...

This paper investigates finite sample properties of localized version of moment estimator including Local Generalized Method of Moments(LGMM) and conditional Euclidean Empirical Likelihood(CEEL) estimator. By comparing the performance of LGMM estimator and C-EEL estimator with various nonparametric techniques including the choice of bandwidth, choice of kernel and trimming strategies, this pape...

A nonparametric mixture model approach to empirical Bayes compound decisions for the Gaussian location model is compared with a parametric empirical Bayes approach recently suggested by Martin and Walker and several recent more formal Bayes procedures. Martin and Walker (2013) have recently proposed a parametric empirical Bayes procedure for the classical Gaussian compound decision problem in w...

In this paper we propose a generalised iterative algorithm for calculating variational Bayesian estimates for a normal mixture model and we investigate its convergence properties. It is shown theoretically that the variational Bayes estimator converges locally to the maximum likelihood estimator at the rate of O(1/n) in the large sample limit. We also demonstrate by numerical experiments that t...

This paper considers the estimation of parameters in a dynamic stochastic model for securities prices, where the expected rate of return is a random variable. An empirical Bayes estimator is developed from the model structure. The estimator is an improvement on other popular estimators in terms of mean squared error. The e ect of reduced estimation error on accumulated wealth is analyzed for th...

An empirical hierarchical Bayes approach was first proposed to analyze gene expression data collected from microarray experiments. Through a closed-form variance shrinkage estimator, information from multiple genes is pooled to increase the statistical power of multiple hypothesis testing. The approach also allows various types of subject matter knowledge to be incorporated conveniently. Caveat...

In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare th...