自适应设计广义线性模型的自适应Lasso惩罚最小二乘的渐近性质

针对自适应设计广义线性模型,研究自适应Lasso惩罚最小二乘变量选择方法。在一定条件下,得到自适应Lasso惩罚最小二乘估计的相合性和Oracle性质,该结果将固定设计广义线性模型相关结果推广到自适应设计广义线性模型中。通过模拟可知,自适应Lasso惩罚方法优于Lasso惩罚方法。

自适应设计广义线性模型下基于Lγ惩罚的变量选择及其渐近理论

本文利用Frank和Friedman[3]提出的Lγ惩罚方法,研究了自适应设计广义线性模型下基于惩罚拟似然的变量选择问题及其渐近性质,该方法可同时进行参数估计和变量选择.当0<γ<1时,在适当条件下,本文证明了自适应广义线性模型下基于Lγ惩罚和拟似然的估计量的存在性、相合性和Oracle性质,该结果将广义线性模下固定设计的相关理论推广到了自适应设计情况.最后,本文通过数值模拟和实际数据分析验证所获得理论的有效性.

Asymptotic Normality of Multi-Dimension Quasi Maximum Likelihood Estimate in Generalized Linear Models withAdaptive Design

We study the quasi likelihood equation in Generalized Linear Models（GLM） with adaptive design ∑（i=1）^n xi（yi-h（x＇iβ））=0, where yi is a q=vector, and xi is a p×q random matrix. Under some assumptions, it is shown that the Quasi- Likelihood equation for the GLM has a solution which is asymptotic normal.

Asymptotic Properties of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Diverging Number of Covariates

In this paper, for the generalized linear models （GLMs） with diverging number of covariates, the asymptotic properties of maximum quasi-likelihood estimators （MQLEs） under some regular conditions are developed. The existence, weak convergence and the rate of convergence and asymptotic normality of linear combination of MQLEs and asymptotic distribution of single linear hypothesis teststatistics are presented. The results are illustrated by Monte-Carlo simulations.

Uniform tail asymptotics for the aggregate claims with stochastic discount in the renewal risk models

Considering an insurer who is allowed to make risk-free and risky investments, as in Tang et al.(2010), the price process of the investment portfolio is described as a geometric L′evy process. We study the tail probability of the stochastic present value of future aggregate claims. When the claim-size distribution is of extended regular variation, we obtain an asymptotically equivalent formula which holds uniformly for all time horizons, and furthermore, the same asymptotic formula holds for the finite-time ruin probabilities. The results extend the works of Tang et al.(2010).

The Asymptotic Properties of Scad Penalized Generalized Linear Models with Adaptive Designs

This paper discusses the asymptotic properties of the SCAD(smoothing clipped absolute deviation)penalized quasi-likelihood estimator for generalized linear models with adaptive designs,which extend the related results for independent observations to dependent observations.Under certain conditions,the authors proved that the SCAD penalized method correctly selects covariates with nonzero coefficients with probability converging to one,and the penalized quasi-likelihood estimators of non-zero coefficients have the same asymptotic distribution they would have if the zero coefficients were known in advance.That is,the SCAD estimator has consistency and oracle properties.At last,the results are illustrated by some simulations.

赖氨酸特异性去甲基酶1反苯环丙胺类不可逆抑制剂的研究进展

组蛋白赖氨酸特异性去甲基酶1(LSD1)在调节组蛋白H3K4、H3K9的甲基化状态中起重要作用,在多种肿瘤细胞中高表达,已成为一种新型抗肿瘤靶点。目前为止,已报道的LSD1抑制剂中对反苯环丙胺(TCP)类抑制剂的研究相对更为深入。综述TCP类抑制剂的作用机制、与辅酶黄素腺嘌呤二核苷酸(FAD)结合模式及设计优化的研究进展。