Variable Selection via Generalized SELO-Penalized Cox Regression Models

The seamless-L_0(SELO) penalty is a smooth function that very closely resembles the L_0 penalty, which has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection. In this paper, the authors first generalize the SELO penalty to a class of penalties retaining good features of SELO, and then develop variable selection and parameter estimation in Cox models using the proposed generalized SELO(GSELO) penalized log partial likelihood(PPL) approach. The authors show that the GSELO-PPL procedure possesses the oracle property with a diverging number of predictors under certain mild, interpretable regularity conditions. The entire path of GSELO-PPL estimates can be efficiently computed through a smoothing quasi-Newton(SQN) with continuation algorithm. The authors propose a consistent modified BIC(MBIC) tuning parameter selector for GSELO-PPL, and show that under some regularity conditions, the GSELOPPL-MBIC procedure consistently identifies the true model. Simulation studies and real data analysis are conducted to evaluate the finite sample performance of the proposed method.     

Unified Variable Selection for Varying Coefficient Models with Longitudinal Data

Variable selection for varying coefficient models includes the separation of varying and constant effects, and the selection of variables with nonzero varying effects and those with nonzero constant effects. This paper proposes a unified variable selection approach called the double-penalized quadratic inference functions method for varying coefficient models of longitudinal data. The proposed method can not only separate varying coefficients and constant coefficients, but also estimate and sele...     

Quasi-Stepwise Regression Variable Selection and its Application in Rural Household Net Income Forecasting

Quasi-Stepwise regression variable selection method based on validation of least absolute deviation regression is proposed. The method shows good performance in its application to rural household net income forecasting. Three models with high accuracy are constructed based on this method. According to these models, factors affecting rural household net income are analyzed, and some policy suggestions are given to increase the rural household net income.     

Rank-Based Test for Partial Functional Linear Regression Models

This paper investigates the hypothesis test of the parametric component in partial functional linear regression models. Based on a rank score function, the authors develop a rank test using functional principal component analysis, and establish the asymptotic properties of the resulting test under null and local alternative hypotheses. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.     

TWO-STAGE ESTIMATION FOR SEEMINGLY UNRELATED NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated （SU） non- parametric regression models. The authors propose a new method to estimate the unknown functions, which is an extension of the two-stage procedure in the longitudinal data framework. The authors show the resulted estimators are asymptotically normal and more efficient than those based on only the individual regression equation. Some simulation studies are given in support of the asymptotic results. A real data from an ongoing environmental epidemiologie study are used to illustrate the proposed procedure.     

Test for Heteroscedasticity in Partially Linear Regression Models

Testing heteroscedasticity determines whether the regression model can predict the dependent variable consistently across all values of the explanatory variables. Since the proposed tests could not detect heteroscedasticity in all cases, more precisely in heavy-tailed distributions, the authors established new comprehensive test statistic based on Levene's test. The authors built the asymptotic normality of the test statistic under the null hypothesis of homoscedasticity based on the recent theory of analysis of variance for the infinite factors level. The proposed test uses the residuals from a regression model fit of the mean function with Levene's test to assess homogeneity of variance. Simulation studies show that our test yields better than other methods in almost all cases even if the variance is a nonlinear function. Finally, the proposed method is implemented through a real data-set.     

Variable Selection for Structural Equation with Endogeneity

This paper studies variable selection problem in structural equation of a two-stage least squares (2SLS) model in presence of endogeneity which is commonly encountered in empirical economic studies. Model uncertainty and variable selection in the structural equation is an important issue as described in Andrews and Lu (2001) and Caner (2009). The authors propose an adaptive Lasso 2SLS estimator for linear structural equation with endogeneity and show that it enjoys the oracle properties, i.e., the consistency in both estimation and model selection. In Monte Carlo simulations, the authors demonstrate that the proposed estimator has smaller bias and MSE compared with the bridge-type GMM estimator (Caner, 2009). In a case study, the authors revisit the classic returns to education problem (Angrist and Krueger, 1991) using the China Population census data. The authors find that the education level not only has strong effects on income but also shows heterogeneity in different age cohorts.     

灰色关联度分析在变量筛选应用中的误区

探讨了多元回归建模中灰色关联度分析应用于变量筛选时存在的问题及其原因 ;结合实例阐明 ,GM(1 ,n )模型本身无法克服模型的多重相关性 ,用灰色关联度分析来筛选变量所获得的 GM(1 ,n )模型是不可靠的.     

一种新的时变多径环境下OFDM系统参数选取方法

分别针对CP长度大于和小于最大多径时延两种情况，分析了由时变多径环境引入的ISI分量和子载波间干扰（ICI）分量，提出了“基于多径时延分布”选取CP长度进而确定子载波数的方法。计算机仿真验证充分说明了该方法的优越性，所得研究结论对于不同信道环境下的实际OFDM系统参数选择具有指导意义。     

基于主基底分析的两阶段变量筛选方法

在实际系统分析及建模中,人们往往需要保留一些特别重要的分析变量。本文改进了基于主基底的变量筛选方法,分两个阶段来筛选系统分析所需变量。用重要变量构建初始主基底超平面,作为筛选其他普通变量的起点。该方法既结合了人们的定性分析经验,又保留了基于主基底分析的变量筛选方法能够自动筛选系统分析所需最简变量集合的特点,达到了数据降维目的。实际案例分析验证了该方法的有效性。     

遗传算法在模糊线性回归子集选择中的应用

针对模糊线性回归,提出子集选择问题,给出评价准则,并采用遗传算法（ＧＡｓ）实现子集选择．最后给出应用实例     

A New Statistic for Variable Selection in Questionnaire Analysis

1　 IntroductionIn questionnaire analysis,there are two important problems.One is called variableselection or assessment of question worth by which we mean a questionnaire that shouldconsist of those questions which are helpful to distinguish the respondents,in anotherword,a question should be discarded if it doesn′t offer sufficient information fordistinguishing the groups.The other is the problem of discriminant analysis,that is,anew respondent should be properly put into a certain group ba…     

基于Jeffreys非信息超先验的贝叶斯变量选择算法

在计算机试验中,复杂现象的仿真拥有数目庞大的输入变量,因此,筛选出对输出影响重要的输入变量是至关重要的。我们通过Jeffreys非信息超先验,提出了一种新的贝叶斯变量选择算法。不同于计算机试验中存在的变量选择算法,我们的方法不需要调节控制稀疏性的超参数。新的变量选择方法通过EM（expectation-maximization）算法求解,试验结果表明,我们的方法不仅取得了理想的效果,而且大大地减少了计算的负担。     

相关MIMO信道下可变数目天线选择算法

提出两种多入多出（MIMO）系统可变数目天线选择算法,来动态适应多种相关MIMO信道环境。基于特征值准则的算法按照最小特征值与特征值总和之比动态选择天线,避免天线间的高度相关性;基于容量准则的算法在保证容量接近最大值的条件下,最小化天线数目。仿真结果表明,两种算法在天线选择后的容量均接近选择前的情况;通过结合链路自适应技术,可进一步得到优于无天线选择系统的数据速率和误码性能。     

组合证券模糊最优化模型的研究

本文提出了一种考虑收益和风险偏好的组合证券模糊最优化模型。给出了最优组合的计算方法和有效边界的表达式。最后用释例说明了方法应用。     

动态投入产出模型在控制变量约束下的反馈控制

动态投入产出模型在控制变量约束下的反馈控制李银国（重庆建筑专科学校，６３００３０）ＦｅｅｄｇｂａｃｋＣｏｎｔｒｏｌｏｆＤｙｎａｍｉｃＩｎｐｕｔ－ＯｕｔｐｕｔＭｏｄｅｌｓｗｉｔｈｔｈｅＣｏｎｔｒｏｌｏｆＶａｒｉａｂｌｅＣｏｎｓｔｒａｉｎｔｓＬｉＹｉｎｇ...     

供应链合作伙伴选择的层次变权多因素决策

针对企业在选择供应链合作伙伴时,常权综合方法难以体现决策者对决策因素的均衡性要求和激励性要求,以及常权综合方法常常违背决策因素间不可替代性的弊病,提出了一种基于可拓理论和变权理论的新方法———层次变权优度评价法.该方法克服了常权综合的弊病,能够对某些重要影响因素进行均衡性处理的同时,对某些关键影响因素进行激励,从而使决策者意愿的体现建立在科学理论的基础之上.经实证研究,证实了该方法应用的灵活性和有效性.     

Empirical Likelihood Test for Regression Coefficients in High Dimensional Partially Linear Models

Journal of Systems Science and Complexity - This paper considers tests for regression coefficients in high dimensional partially linear Models. The authors first use the B-spline method to estimate...     

组合证券资产选择模糊最优化模型研究

提出了考虑收益和风险偏好的更为一般的组合证券资产选择模糊最优化模型。给出了最优投资比例的计算公式。并研究了以风险偏好,固定利率为参数的组合证券的有效边界的变动。最后以释例说明了方法的应用。     

复杂曲面拟合的相关向量机模型及其泛化能力

介绍相关向量机回归(RVM,Relevance Vector Machine)的基本原理。分析采用高斯径向基核函数时,核函数参数与模型性能之间的关系,并给出核函数参数选择建议。针对一个复杂非线性函数,采用均匀网格取样,并分别增加正态分布噪声和均匀分布噪声,分析RVM回归分析的拟合和泛化能力并与支持向量机(SVM,Support Vector Machine)回归模型作了比较。结果表明,SVM对样本数据的拟合能力优于RVM。但对于非训练样本,RVM的泛化能力要优于SVM,而且RVM模型更加稀疏,且在给出预测值的同时能够给出预测值的置信区间。