一类广义(2+1)维非自治长短波方程的整体光滑解的存在唯一性

利用先验估计和Galerkin方法,研究了非线性广义耗散(2+1)维非自治长短波方程在H4per(Ω)×H3per(Ω)上光滑解的整体存在唯一性.     

中国上市公司监事会规模和构成的影响因素

理论分析表明,监事会规模及构成内生于公司风险。文章构建联立方程模型实证考察了监事会规模和构成的影响因素,在控制了内生性和制度因素影响后发现：公司经营范围、监督收益对监事会规模和职工监事比例有显著正向影响;监督成本、管理者权力对监事会规模和职工监事比例有显著负向影响。还发现：在控制了监事会规模和构成的影响因素后,监事会规模和职工监事比例与公司风险并没有统计意义上的相关性,表明监事会未能有效发挥降低公司风险的治理作用。因此,加强监事会能力建设,推进公司治理体系和治理能力现代化,仍将是中国公司治理改革努力的方向。     

Smooth-Threshold GEE Variable Selection in High-Dimensional Partially Linear Models with Longitudinal Data

We consider the problem of variable selection in high-dimensional partially linear models with longitudinal data. A variable selection procedure is proposed based on the smooth-threshold generalized estimating equation (SGEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE. We establish the asymptotic properties in a high-dimensional framework where the number of covariates p_n increases as the number of clusters n increases. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure.     

一类椭圆型方程广义解的Holder连续性

本文证明了下面方程的广义解ｕ∈（ａ，Ｇ）∩Ｌ∞（Ｇ）在Ｇ的Ｈｏｌｄｅｒ连续性。关于Ａ和Ｂ，要求满足如下的结构不等式。     

关于一类发展方程Hamilton正则形式的实现

本文首先利用前人的重要结论巧妙地给出了一类发展方程Hamilton正则形式规范化的实现步骤;其次,对特殊三阶Hamilton算子确定的发展方程化为正则形式做了进一步的推广;最后,通过3个算例验证了本文方法的可行性。     

A Characterization of the Normal Family of Distributions Based on the Bias of the Maximum Likelihood Estimator

This paper characterizes the family of Normal distributions within the class of exponential families of distributions, via the structure of the bias of the maximum likelihood estimator Θ _n of the canonical parameter Θ . More specifically, when E _θ ( Θ n ) – Θ = (1/ n ) Q ( Θ ) + o (1/ n ), the equality Q ( Θ ) = 0 proves to be a property of the Normal distribution only. The same conclusion is obtained for the one-dimensional case bt assuming that Q ( Θ ) is a polynomial of Θ .     

Asymptotic Analysis of the Sieve Estimator for a Class of Parabolic SPDEs

In this paper we consider the problem of estimating a coefficient of a strongly elliptic partial differential operator in stochastic parabolic equations. The coefficient is a bounded function of time. We compute the maximum likelihood estimate of the function on an approximating space (sieve) using a finite number of the spatial Fourier coefficients of the solution and establish conditions that guarantee consistency and asymptotic normality of the resulting estimate as the number of the coefficients increases. The equation is assumed diagonalizable in the sense that all the operators have a common system of eigenfunctions.     

Matrix-analytic Models and their Analysis

We survey phase-type distributions and Markovian point processes, aspects of how to use such models in applied probability calculations and how to fit them to observed data. A phase-type distribution is defined as the time to absorption in a finite continuous time Markov process with one absorbing state. This class of distributions is dense and contains many standard examples like all combinations of exponential in series/parallel. A Markovian point process is governed by a finite continuous time Markov process (typically ergodic), such that points are generated at a Poisson intensity depending on the underlying state and at transitions; a main special case is a Markov-modulated Poisson process. In both cases, the analytic formulas typically contain matrix-exponentials, and the matrix formalism carried over when the models are used in applied probability calculations as in problems in renewal theory, random walks and queueing. The statistical analysis is typically based upon the EM algorithm, viewing the whole sample path of the background Markov process as the latent variable.     

Estimation in an empirical bayes model for longitudinal and cross‐sectionally clustered binary data

Some studies generate data that can be grouped into clusters in more than one way. Consider for instance a smoking prevention study in which responses on smoking status are collected over several years in a cohort of students from a number of different schools. This yields longitudinal data, also cross‐sectionaliy clustered in schools. The authors present a model for analyzing binary data of this type, combining generalized estimating equations and estimation of random effects to address the longitudinal and cross‐sectional dependence, respectively. The estimation procedure for this model is discussed, as are the results of a simulation study used to investigate the properties of its estimates. An illustration using data from a smoking prevention trial is given.