Asymptotics of Estimating Equations under Natural Conditions

In a variety of statistical problems one needs to solve an equation in order to get an estimator. We consider the large sample properties of such estimators generated from samples that are not necessarily identically distributed. Very general assumptions that lead to the existence, strong consistency, and asymptotic normality of the estimators are given. A number of results that are useful in verifying the general assumptions are given and an example illustrates their use. General applications to maximum likelihood, iteratively reweighted least squares, and robust estimation are discussed briefly.     

Variable selection using penalized empirical likelihood

This paper considers variable selection for moment restriction models.We propose a penalized empirical likelihood(PEL) approach that has desirable asymptotic properties comparable to the penalized likelihood approach,which relies on a correct parametric likelihood specification.In addition to being consistent and having the oracle property,PEL admits inference on parameter without having to estimate its estimator's covariance.An approximate algorithm,along with a consistent BIC-type criterion for selecting ...     

Kumaraswamy Binomial分布

在Binomial模型中考虑over-dispersion时,每一个独立事件成功的概率通常被视为一个连续随机变量.在本文中,我们提出了成功概率服从Kumaraswamy分布的混合Binomial模型.讨论了这个模型的随机序和相依性;并用数据来拟合这些模型,数值计算结果表明在拟合某些数据时KB模型比BB模型拟合效果更好.     

关于随机选择的一个强极限定理

本文利用似然比和区间剖分法研究相依Ｎ值随机变量序列选择的极限性质,得到关于赌博方法的一个强极限定理的若干推广。     

A note on the simple structural regression model

In this paper we investigate some aspects like estimation and hypothesis testing in the simple structural regression model with measurement errors. Use is made of orthogonal parametrizations obtained in the literature. Emphasis is placed on some properties of the maximum likelihood estimators and also on the distribution of the likelihood ratio statistics.     

Adaptive anisotropic noise filtering for magnitude MR data

Conventional noise filtering schemes applied to magnitude magnetic resonance (MR) images tacitly assume Gauss distributed noise. Magnitude MR data, however, are Rice distributed. Not incorporating this knowledge leads inevitably to biased results, in particular when applying such filters in regions with low signal-to-noise ratio. In this work, we show how the Rice data probability distribution can be incorporated so as to construct a noise filter that is far less biased.