Sparse estimation and inference for censored median regression

Censored median regression has proved useful for analyzing survival data in complicated situations, say, when the variance is heteroscedastic or the data contain outliers. In this paper, we study the sparse estimation for censored median regression models, which is an important problem for high dimensional survival data analysis. In particular, a new procedure is proposed to minimize an inverse-censoring-probability weighted least absolute deviation loss subject to the adaptive LASSO penalty and result in a sparse and robust median estimator. We show that, with a proper choice of the tuning parameter, the procedure can identify the underlying sparse model consistently and has desired large-sample properties including root-n consistency and the asymptotic normality. The procedure also enjoys great advantages in computation, since its entire solution path can be obtained efficiently. Furthermore, we propose a resampling method to estimate the variance of the estimator. The performance of the procedure is illustrated by extensive simulations and two real data applications including one microarray gene expression survival data.     

Goodness-of-fit tests for general linear models with covariates missed at random

In this paper, we consider a model checking problem for general linear models with randomly missing covariates. Two types of score type tests with inverse probability weight, which is estimated by parameter and nonparameter methods respectively, are proposed to this goodness of fit problem. The asymptotic properties of the test statistics are developed under the null and local alternative hypothesis. Simulation study is carried out to present the performance of the sizes and powers of the tests. We illustrate the proposed method with a data set on monozygotic twins.     

Inverse Problems of Matroid Intersection

In this paper we study the inverse problem of matroid intersection: Two matroids M ₁ = (E, ₁) and M ₂ = (E, ₂), their intersection B, and a weight function w on E are given. We try to modify weight w, optimally and with bounds, such that B becomes a maximum weight intersection under the modified weight. It is shown in this paper that the problem can be formulated as a combinatorial linear program and can be further transformed into a minimum cost circulation problem. Hence it can be solved by strongly polynomial time algorithms. We also give a necessary and sufficient condition for the feasibility of the problem. Finally we extend the discussion to the version of the problem with Multiple Intersections.     

基于多维矩阵项目组织在某国防项目中的应用

按照型号国防项目管理的要求,在某大型国防项目管理实践中项目组织形式组建进行了探讨。本文首先分析当前各种项目管理组织形式特点,在分析本单位实际情况的基础上,针对型号国防项目管理的特殊需求,提出了一种多维矩阵的项目组织,并就该组织机构在运行中出现的一些问题进行了分析,探讨了该种组织形式的优缺点,提出了进一步的改进问题的办法和措施。     

蕴含在函数矩阵中的积分不等式

以平均值不等式为基础,获得正值连续函数矩阵中的一个积分不等式公式,利用此公式,布涅可夫斯基(V.J.Buniakowski)等积分不等式可通过构作矩阵进行直观明了的证明;利用此公式,对所有的正值连续函数矩阵可构造出相应的积分不等式.     

人口预测的随机方法:基于Leslie矩阵和ARMA模型

本文探讨了人口预测的一种随机方法。文章回顾了经典的Leslie矩阵并结合中国的人口统计数据,用时间序列的ARMA模型对未来的生育率、死亡率进行估计,并由此构造Les-lie矩阵,经时间序列的数据中心化,根据自相关函数、偏自相关函数的截尾性或拖尾性,以及贝叶斯信息准则函数方法对模型定阶,实现对ARMA模型的识别。在中国人口预测方面的应用证明,基于Leslie矩阵和ARMA模型的人口随机预测方法是稳健的,具有很强的适用性。由于统计数据可获得性的局限,对模型做了不少假设和近似。随着人口数据的积累,未来将会在此方面有所改进。     

一类扩展的投入产出模型

 内容提要：基于居民部门封闭的投入产出模型进行扩展,并将政府部门从最终需求列中转移出来,纳入到生产部门,列入投入产出表的第Ⅰ象限,政府部门所在的行是以货币形式表现的各部门（包括居民部门）的税收支付,政府部门所在的列是政府对各个部门的各种消费品和劳务的消费额,得到了扩展的局部闭投入产出模型,并在此模型的基础上得到了各种乘数。进一步将投资考虑进去得到动态投入产出扩展模型,使得国民经济各个生产部门、居民部门、政府部门成为一个完整的投入产出平衡体。     

A non-iterative posterior sampling algorithm for Laplace linear regression model

In this article, a non-iterative sampling algorithm is developed to obtain an independently and identically distributed samples approximately from the posterior distribution of parameters in Laplace linear regression model. By combining the inverse Bayes formulae, sampling/importance resampling, and expectation maximum algorithm, the algorithm eliminates the diagnosis of convergence in the iterative Gibbs sampling and the samples generated from it can be used for inferences immediately. Simulations are conducted to illustrate the robustness and effectiveness of the algorithm. Finally, real data are studied to show the usefulness of the proposed methodology.     

Exponentiated half logistic distribution: Different estimation methods and joint confidence regions

In this article, we study the problem of estimating the unknown shape and scale parameters of the exponentiated half logistic distribution. For the maximum-likelihood estimation, we obtain a necessary and sufficient condition for the existence and uniqueness of maximum-likelihood estimates of the parameters. Inverse moment and modified inverse moment estimators are derived. Monte Carlo simulations are conducted to compare their performances. Two methods for constructing joint confidence regions for the two parameters are also proposed and their performances are discussed. A numerical example is presented to illustrate the methods.     

Transformed GARMA model: Properties and simulations

Real time series can present anomalies, like non-additivity, non-normality, and heteroscedasticity, which makes using GARMA models impossible. Our article introduces a new class of models called Transformed Generalized Autoregressive Moving Average (TGARMA) models that allow using transformations to guarantee the GARMA assumptions. We present an extensive simulation study of the influence of the transformation on GARMA estimation. We also propose using bootstrap methods to get more information about the distribution of the transformation parameter. We apply the methodology to data related to annual Swedish fertility rates.