SEQUENTIAL,BOTTOM‐UP VARIABLE SELECTION FOR HIGH‐DIMENSIONAL CLASSIFICATION

Most methods for variable selection work from the top down and steadily remove features until only a small number remain. They often rely on a predictive model, and there are usually significant disconnections in the sequence of methodologies that leads from the training samples to the choice of the predictor, then to variable selection, then to choice of a classifier, and finally to classification of a new data vector. In this paper we suggest a bottom‐up approach that brings the choices of variable selector and classifier closer together, by basing the variable selector directly on the classifier, removing the need to involve predictive methods in the classification decision, and enabling the direct and transparent comparison of different classifiers in a given problem. Specifically, we suggest ‘wrapper methods’, determined by classifier type, for choosing variables that minimize the classification error rate. This approach is particularly useful for exploring relationships among the variables that are chosen for the classifier. It reveals which variables have a high degree of leverage for correct classification using different classifiers; it shows which variables operate in relative isolation, and which are important mainly in conjunction with others; it permits quantification of the authority with which variables are selected; and it generally leads to a reduced number of variables for classification, in comparison with alternative approaches based on prediction.     

Nonconcave penalized estimation for partially linear models with longitudinal data

A nonconcave penalized estimation method is proposed for partially linear models with longitudinal data when the number of parameters diverges with the sample size. The proposed procedure can simultaneously estimate the parameters and select the important variables. Under some regularity conditions, the rate of convergence and asymptotic normality of the resulting estimators are established. In addition, an iterative algorithm is proposed to implement the proposed estimators. To improve efficiency for regression coefficients, the estimation of the covariance function is integrated in the iterative algorithm. Simulation studies are carried out to demonstrate that the proposed method performs well, and a real data example is analysed to illustrate the proposed procedure.     

Dynamic latent trait models with mixed hidden Markov structure for mixed longitudinal outcomes

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMMs). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study to jointly model questionnaire-based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.     

A model-averaging treatment of multiple instruments in Poisson models with errors

We analyze Poisson regression when covariates contain measurement errors and when multiple potential instrumental variables are available. Without empirical knowledge to select the most suitable variable as an instrument, we propose a novel model-averaging approach to resolve this issue. We prescribe an implementation and establish its optimality in terms of minimizing prediction risk. We further show that, as long as one model is correctly specified among all potential instrumental variable models, our method will lead to consistent prediction. The performance of our method is illustrated through simulations and a movie sales example.     

RCEV heteroscedasticity test based on the studentized residuals

All the usual heteroscedasticity tests in the statistics and econometrics literature are based on raw residuals. Although the raw residuals are heteroscedastic, studentized residuals can still be homoscedastic. In this study, the version of Çelik’s RCEV heteroscedasticity test which is based on studentized residuals is introduced.     

Partial Identification in Triangular Systems of Equations With Binary Dependent Variables

This paper studies the special case of the triangular system of equations in Vytlacil and Yildiz (2007), where both dependent variables are binary but without imposing the restrictive support condition required by Vytlacil and Yildiz (2007) for identification of the average structural function (ASF) and the average treatment effect (ATE). Under weak regularity conditions, we derive upper and lower bounds on the ASF and the ATE. We show further that the bounds on the ASF and ATE are sharp under some further regularity conditions and an additional restriction on the support of the covariates and the instrument.     

马克思技术创新思想再解读

通过对马克思著作文本的解读,可以看出,马克思不仅从可变资本和不变资本节约的角度对技术创新作了分类,而且从自然、经济、社会、人的广阔视野出发,全面审视了技术创新的功能,论证了技术创新是人类社会发展的原动力。马克思的技术创新思想进一步证明和发展了唯物史观的科学原理,深刻揭露了资本家剥削工人的秘密,奠定了科学社会主义理论的基础。     

一类高阶非线性泛函方程的振动准则

研究了变系数高阶非线性泛函方程x（g（t））=P（t）x（t）＋ m∑im1 Qi（t）x（gK＋i（t））,的解的振动性。得到了一些新的振动准则．所得蛄论推广了目前已有结果,此外,给出了新振动准则在差分方程中的一些应用．     

农户对订单农业的续单意愿及其影响因素——以湖北省油菜制种为例

订单农业是推进我国农业现代化发展的重要方式,然而近年来却出现了农户参与度降低、满意度不高的问题。文章以农户的续单意愿为切入点、以湖北省22个行政村制种农户的调查数据为基础,对农户续单意愿及其影响因素进行了分析,并创新性地引入农户对订单农业的满意度作为中介变量,构建了农户对订单农业续单意愿的影响模型。结论认为龙头企业对订单执行过程的管理、生产设施与生产条件、生产要求和质量标准、政府的支持与组织、订单农业的比较效益、订单的收入等因素都显著影响农户对订单农业的参与意愿,而农户前期参与订单农业的满意度对上述影响因素与续单意愿之间的关系又存在着显著的中介效应。最后结合分析结果给出了相应的政策建议。     

Bayesian Tobit quantile regression using g-prior distribution with ridge parameter

A Bayesian approach is proposed for coefficient estimation in the Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients. The prior is generalized by introducing a ridge parameter to address important challenges that may arise with censored data, such as multicollinearity and overfitting problems. Then, a stochastic search variable selection approach is proposed for Tobit quantile regression model based on g-prior. An expression for the hyperparameter g is proposed to calibrate the modified g-prior with a ridge parameter to the corresponding g-prior. Some possible extensions of the proposed approach are discussed, including the continuous and binary responses in quantile regression. The methods are illustrated using several simulation studies and a microarray study. The simulation studies and the microarray study indicate that the proposed approach performs well.