Two-step variable selection in partially linear additive models with time series data

Lots of semi-parametric and nonparametric models are used to fit nonlinear time series data. They include partially linear time series models, nonparametric additive models, and semi-parametric single index models. In this article, we focus on fitting time series data by partially linear additive model. Combining the orthogonal series approximation and the adaptive sparse group LASSO regularization, we select the important variables between and within the groups simultaneously. Specially, we propose a two-step algorithm to obtain the grouped sparse estimators. Numerical studies show that the proposed method outperforms LASSO method in both fitting and forecasting. An empirical analysis is used to illustrate the methodology.     

Confidence intervals for a two-parameter exponential distribution: One- and two-sample problems

The problems of interval estimating the mean, quantiles, and survival probability in a two-parameter exponential distribution are addressed. Distribution function of a pivotal quantity whose percentiles can be used to construct confidence limits for the mean and quantiles is derived. A simple approximate method of finding confidence intervals for the difference between two means and for the difference between two location parameters is also proposed. Monte Carlo evaluation studies indicate that the approximate confidence intervals are accurate even for small samples. The methods are illustrated using two examples.     

Bayesian inference of three-parameter bathtub-shaped lifetime distribution

We investigate a Bayesian inference in the three-parameter bathtub-shaped lifetime distribution which is obtained by adding a power parameter to the two-parameter bathtub-shaped lifetime distribution suggested by Chen (2000). The Bayes estimators under the balanced squared error loss function are derived for three parameters. Then, we have used Lindley's and Tierney–Kadane approximations (see Lindley 1980; Tierney and Kadane 1986) for computing these Bayes estimators. In particular, we propose the explicit form of Lindley's approximation for the model with three parameters. We also give applications with a simulated data set and two real data sets to show the use of discussed computing methods. Finally, concluding remarks are mentioned.     

Linear Models with Doubly Exchangeable Distributed Errors

We study the general linear model (GLM) with doubly exchangeable distributed error for m observed random variables. The doubly exchangeable general linear model (DEGLM) arises when the m-dimensional error vectors are “doubly exchangeable,” jointly normally distributed, which is a much weaker assumption than the independent and identically distributed error vectors as in the case of GLM or classical GLM (CGLM). We estimate the parameters in the model and also find their distributions. We show that the tests of intercept and slope are possible in DEGLM as a particular case using parametric bootstrap as well as multivariate Satterthwaite approximation.     

圆的面积公式的新证明——验证圆的面积公式S=πr^2

本文应用阿基米德的内外正多边形逼近法和开普勒的无穷分割法,站在后人的肩膀上,运用极限理论,洛比达法则,以及收敛法则,夹逼法则,三角函数等近代数学的办法,来为开普勒对上述问题做个解释,从而验证圆的面积计算公式确实为S=πr^2     

基于原型逼近法的招标代理机构转化业主需求机理

为了使编制的招标文件真实地反映业主的需求,提高招标文件的编制质量,促进招标代理机构自身业务水平的提升,利用原型逼近的方法,设计出项目需求的转化机理,确定项目初始需求、还原逼近真实需求,并在招标文件的编写中予以体现,达到业主需求的最终转化目的。     

模糊概率决策系统的属性约简

基于模糊等价关系及论域上的概率分布建立模糊概率信息系统,并给出了模糊概率信息系统的熵与条件熵.最后基于模糊概率信息系统的条件熵提出模糊概率决策信息系统的属性约简并给出了算法.     

独立逆抽样下优势比的置信区间

本文对独立逆抽样设计下优势比的置信区间的构造进行了研究,包括三个已有的方法,以及本文引入的鞍点逼近方法。通过模拟比较了这四个方法给出的置信区间。模拟结果表明,基于鞍点逼近方法给出的置信区间不比另外三种方法差。并且在一些情况下表现还优于其它三个方法。     

Symbolic computation for approximating distributions of some families of one and two-sample nonparametric test statistics

A conditional saddlepoint approximation was provided by Gatto and Jammalamadaka (1999) for computing the distribution function of many test statistics based on dependent quantities like multinomial frequencies, spacing frequencies, etc. The considerable complexity of the formulas involved can be bypassed by symbolic computation. This article illustrates the effectiveness of symbolic computation to evaluate the saddlepoint approximation for the likelihood ratio, the exponential score, and the Wald-Wolfowitz test statistics. The case of composite hypotheses is also discussed.