arXiv subjects

J. T. Gene Hwang

Publications and source records attributed to J. T. Gene Hwang.

3 recordsLinked to original sources

arXiv2015

A new approach for analyzing panel AR(1) series with application to the unit root test

This paper derives several novel tests to improve on the t-test for testing AR(1) coefficients of panel time series, i.e., of multiple time series, when each has a small number of observations. These tests can determine the acceptance or the rejection of each hypothesis individually while controlling the average type one error. Strikingly, the testing statistics derived by the empirical Bayes approach can be approximated by a simple form similar to the t-statistic; the only difference is that the means and the variances are estimated by shrinkage estimators. Simulations demonstrate that the proposed tests have higher average power than the t-test in all settings we examine including those when the priors are miss-specified and the cross section series are dependent.

math.ST↗

arXiv2012

Shrinkage Confidence Procedures

The possibility of improving on the usual multivariate normal confidence was first discussed in Stein (1962). Using the ideas of shrinkage, through Bayesian and empirical Bayesian arguments, domination results, both analytic and numerical, have been obtained. Here we trace some of the developments in confidence set estimation.

stat.ME↗

arXiv2005

Minimax estimation with thresholding and its application to wavelet analysis

Many statistical practices involve choosing between a full model and reduced models where some coefficients are reduced to zero. Data were used to select a model with estimated coefficients. Is it possible to do so and still come up with an estimator always better than the traditional estimator based on the full model? The James-Stein estimator is such an estimator, having a property called minimaxity. However, the estimator considers only one reduced model, namely the origin. Hence it reduces no coefficient estimator to zero or every coefficient estimator to zero. In many applications including wavelet analysis, what should be more desirable is to reduce to zero only the estimators smaller than a threshold, called thresholding in this paper. Is it possible to construct this kind of estimators which are minimax? In this paper, we construct such minimax estimators which perform thresholding. We apply our recommended estimator to the wavelet analysis and show that it performs the best among the well-known estimators aiming simultaneously at estimation and model selection. Some of our estimators are also shown to be asymptotically optimal.

math.ST↗