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| 1 | Statistical inference on parametric part for partially linear single-index model显示文摘Statistical inference on parametric part for the partially linear single-index model (PLSIM) is considered in this paper. A profile least-squares technique for estimating the parametric part is proposed and the asymptotic normality of the profile least-squares estimator is given. Based on the estimator, a generalized likelihood ratio (GLR) test is proposed to test whether parameters on linear part for the model is under a contain linear restricted condition. Under the null model, the proposed GLR statistic follows asymptotically the χ2-distribution with the scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Both simulated and real data examples are used to illustrate our proposed methods. | ZHANG RiQuan HUANG ZhenSheng | 2009 | Science China Mathematics2009，52，10： | 5 |
| 2 | The Adaptive LASSO Spline Estimation of Single-Index Model显示文摘In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L1 penalty. The calculation methods of the proposed estimators are given on the basis of the known lars algorithm. Under some regular conditions, the authors demonstrate the asymptotic properties of the proposed estimators and the oracle properties of adaptive LASSO(aL ASSO) variable selection. Simulations are used to investigate the performances of the proposed estimator and illustrate that it is effective for simultaneous variable selection as well as estimation of the single-index models. | LU Yiqiang ZHANG Riquan HU Bin | 2016 | Journal of Systems Science & Complexity2016，29，4： | 4 |
| 3 | Feature Screening for Nonparametric and Semiparametric Models with Ultrahigh-Dimensional Covariates显示文摘 | ZHANG Junying ZHANG Riquan ZHANG Jiajia | 2018 | Journal of Systems Science & Complexity2018，31，5： | 2 |
| 4 | Robust Estimation and Variable Selection for Semiparametric Partially Linear Varying Coefficient Model Based on Modal Regression显示文摘 | Zhang Riquan Zhao Weihua Liu Jical | 2013 | Journal of Nonparametrie Statistics2013，，2： | 1 |
| 5 | Varying-coefficient single-index model显示文摘 | Wong Heung Cheng Wai Zhang Riquan | 2008 | Computational Statistics & Data Analysis2008，52，： | 1 |
| 6 | Variable Selection of Varying Dispersion Student-t Regression Models显示文摘The Student-t regression model is a useful extension of the normal model,which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and provides robust estimation of means and regression coefficients.In this paper,the varying dispersion Student-t regression model is discussed,in which both the mean and the dispersion depend upon explanatory variables.The problem of interest is simultaneously select significant variables both in mean and dispersion model.A unified procedure which can simultaneously select significant variable is given.With appropriate selection of the tuning parameters,the consistency and the oracle property of the regularized estimators are established.Both the simulation study and two real data examples are used to illustrate the proposed methodologies. | ZHAO Weihua ZHANG Riquan | 2015 | Journal of Systems Science & Complexity2015，28，4： | 1 |
| 7 | Group screening for ultra-high-dimensional feature under linear model显示文摘Ultra-high-dimensional data with grouping structures arise naturally in many contemporary statistical problems,such as gene-wide association studies and the multi-factor analysis-of-variance(ANOVA).To address this issue,we proposed a group screening method to do variables selection on groups of variables in linear models.This group screening method is based on a working independence,and sure screening property is also established for our approach.To enhance the finite sample performance,a data-driven thresholding and a two-stage iterative procedure are developed.To the best of our knowledge,screening for grouped variables rarely appeared in the literature,and this method can be regarded as an important and non-trivial extension of screening for individual variables.An extensive simulation study and a real data analysis demonstrate its finite sample performance. | Yong Niu Riquan Zhang Jicai Liu Huapeng Li | 2020 | Statistical Theory and Related Fields2020，4，1： | 1 |
| 8 | Testing for the parametric parts in a single-index varying-coefficient model显示文摘Single-index varying-coefficient models (SIVCMs) are very useful in multivariate nonparametric regression.However,there has less attention focused on inferences of the SIVCMs.Using the local linear method,we propose estimates of the unknowns in the SIVCMs.In this article,our main purpose is to examine whether the generalized likelihood ratio (GLR) tests are applicable to the testing problem for the index parameter in the SIVCMs.Under the null hypothesis our proposed GLR statistic follows the chi-squared distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters or functions,which is called as Wilks' phenomenon (see Fan et al.,2001).A simulation study is conducted to illustrate the proposed methodology. | HUANG ZhenSheng ZHANG RiQuan | 2012 | Science China Mathematics2012，55，5： | 0 |
| 9 | Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data显示文摘This paper considers the iterative sequential lasso(ISLasso)variable selection for generalized linear model with ultrahigh dimensional feature space.The ISLasso selects features by estimated parameter sequentially iteratively for the second order approximation of likelihood function where the features selected depend on regulatory parameters.The procedure stops when extended BIC(EBIC)reaches a minimum.Simulation study demonstrates that the new method is a desirable approach over other methods. | ZHANG Junying WANG Hang ZHANG Riquan ZHANG Jiajia | 2020 | Journal of Systems Science & Complexity2020，33，2： | 0 |
| 10 | Integrated Square Error of Hazard Rate Estimation for Survival Data with Missing Censoring Indicators显示文摘The problem of hazard rate estimation under right-censored assumption has been investigated extensively.Integrated square error(ISE)of estimation is one of the most widely accepted measurements of the global performance for nonparametric kernel estimation.But there are no results available for ISE of hazard rate estimation under right-censored model with censoring indicators missing at random(MAR)so far.This paper constructs an imputation estimator of the hazard rate function and establish asymptotic normality of the ISE for the kernel hazard rate estimator with censoring indicators MAR.At the same time,an asymptotic representation of the mean integrated square error(MISE)is also presented.The finite sample behavior of the estimator is investigated via one simple simulation. | ZOU Yuye FAN Guoliang ZHANG Riquan | 2021 | Journal of Systems Science & Complexity2021，34，2： | 0 |
| 11 | INFERENCE ON COEFFICIENT FUNCTION FOR VARYING-COEFFICIENT PARTIALLY LINEAR MODEL显示文摘在处理 multivariate 数据的一个重要模型是变化系数部分线性的回归模型。在这份报纸,概括可能性的比率测试被开发测试它的系数功能是否正在变化。规范的建议测试 asymptotically 在空假设下面跟随 2 分发和 theWilks 现象,这被显示出,并且它的 asymptotic 力量为测试的 nonparametric 假设完成集中的最佳的率。一些模拟研究说明测试工作很好。 | Jingyan FENG Riquan ZHANG | 2012 | Journal of Systems Science & Complexity2012，25，6： | 0 |
| 12 | Statistical estimation in partially nonlinear models with random effects显示文摘In this article, a partially nonlinear model with random effects is proposed and its new estimation procession is provided. In order to estimate the link function, we propose generalised leastsquare estimate and B-splines estimate methods. Further, we also use the Gauss–Newton methodto construct the estimates of unknown parameters. Finally, we also consider the estimation forthe variance components. The consistency and the asymptotic normality of the estimator will beproved. Simulated and real examples are given to illustrate our proposed methodology, whichshows that our methods give effective estimation. | Ye Que Zhensheng Huang Riquan Zhang | 2017 | Statistical Theory and Related Fields2017，1，2： | 0 |
| 13 | Partially Linear Single-Index Model in the Presence of Measurement Error显示文摘The partially linear single-index model(PLSIM) is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates. This paper considers the PLSIM with measurement error possibly in all the variables. The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function. The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method, and apply it to analyze the osteoporosis prevention data. | LIN Hongmei SHI Jianhong TONG Tiejun ZHANG Riquan | 2022 | Journal of Systems Science & Complexity2022，35，6： | 0 |
| 14 | Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data显示文摘Treatment selection based on patient characteristics has been widely recognised in modern medicine.In this paper,we propose a generalised partially linear single-index mixed-effects modelling strategy for treatment selection and heterogeneous treatment effect estimation in longitudinal clinical and observational studies.We model the treatment effect as an unknown functional curve of a weighted linear combination of time-dependent covariates.This method enables us to investigate covariate-specific treatment effects and make personalised treatment selection in a flexible fashion.We develop a method that combines local linear regression and penalised quasi-likelihood to estimate the weight for each covariate,the unknown treatment effect curve and the parameters for mixed-effects.Based on pointwise confidence intervals for the treatment effect curve,we can make individualised treatment decisions from the information of patient characteristics.A simulation study is conducted to evaluate finite sample performance of the proposed method.We also illustrate the method via analysis of a real data example. | Yanghui Liu Riquan Zhang Shujie Ma Xiuzhen Zhang | 2021 | Statistical Theory and Related Fields2021，5，3： | 0 |
| 15 | Robust sequential design for piecewise-stationary multi-armed bandit problem in the presence of outliers显示文摘The multi-armed bandit(MAB)problem studies the sequential decision making in the presence of uncertainty and partial feedback on rewards.Its name comes from imagining a gambler at a row of slot machines who needs to decide the best strategy on the number of times as well as the orders to play each machine.It is a classic reinforcement learning problem which is fundamental to many online learning problems.In many practical applications of the MAB,the reward distributions may change at unknown time steps and the outliers(extreme rewards)often exist.Current sequential design strategies may struggle in such cases,as they tend to infer additional change points to fit the outliers.In this paper,we propose a robust change-detection upper confidence bound(RCD-UCB)algorithm which can distinguish the real change points from the outliers in piecewise-stationary MAB settings.We show that the proposed RCD-UCB algorithm can achieve a nearly optimal regret bound on the order of O(√SKT log T),where T is the number of time steps,K is the number of arms and S is the number of stationary segments.We demonstrate its superior performance compared to some state-of-the-art algorithms in both simulation experiments and real data analysis.(See http://gffzz188fe103f8f1460asckp5fnnoknok695f.ffgz.tsg.suse.edu.cn/woaishufenke/MAB_STRF.git for the codes used in this paper.) | Yaping Wang Zhicheng Peng Riquan Zhang Qian Xiao | 2021 | Statistical Theory and Related Fields2021，5，2： | 0 |
| 16 | A review of distributed statistical inference显示文摘The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical methods.Meanwhile,it provides opportunities for researchers to develop novel algorithms.Inspired by the idea of divide-and-conquer,various distributed frameworks for statistical estimation and inference have been proposed.They were developed to deal with large-scale statistical optimization problems.This paper aims to provide a comprehensive review for related literature.It includes parametric models,nonparametric models,and other frequently used models.Their key ideas and theoretical properties are summarized.The trade-off between communication cost and estimate precision together with other concerns is discussed. | Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yan Riquan Zhang | 2022 | Statistical Theory and Related Fields2022，6，2： | 0 |
| 17 | Rejoinder on‘A review of distributed statistical inference’显示文摘We thank the editor,Professor Jun Shao,for orga-nizing this stimulating discussion.We are grateful to all discussants for their insightful comments on our review article on the distributed statistical inference.Due to the urgent need to process the datasets with massive sizes,various distributed computing methods have been proposed for the large-scale statistical prob-lems.Meanwhile,some important theoretical results were established.While we want to give a relatively comprehensive overview on this hot topic,there are still some important works that have been missed in our review written over a year ago.However,we are glad to see the discussants provide reviews of some new works and references.We hope that these discussions and our review would serve as a stimulus for further studies in this rapidly developing area. | Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yana Riquan Zhang | 2022 | Statistical Theory and Related Fields2022，6，2： | 0 |
| 18 | ESTIMATION ON SEMIVARYING COEFFICIENT MODELS WITH DIFFERENT DEGREES OF SMOOTHNESS显示文摘Semivarying coefficient models are frequently used in statistical models.In this paper,under the condition that the coefficient functions possess different degrees of smoothness,a two-stepmethod is proposed.In the case,one-step method for the smoother coefficient functions cannot beoptimal.This drawback can be repaired by using the two-step estimation procedure.The asymptoticmean-squared error for the two-step procedure is obtained and is shown to achieve the optimal rate ofconvergence.A few simulation studies are conducted to evaluate the proposed estimation methods. | Riquan ZHANG Jingyan FENG Kaichun WEN Jianhua DING | 2009 | Journal of Systems Science & Complexity2009，22，3： | 0 |
| 19 | The abstract of doctoral dissertation‘nonlinear wavelet density estimation and hazard rate estimation with data missing at random’显示文摘In this thesis,we establish non-linear wavelet density estimators and studying the asymptotic properties of the estimators with data missing at random when covariates are present.The outstanding advantage of non-linear wavelet method is estimating the unsoothed functions,however,the classical kernel estimation cannot do this work.At the same time,we study the larger sample properties of the ISE for hazard rate estimator. | Yuye Zou Guoliang Fan Riquan Zhang | 2020 | Statistical Theory and Related Fields2020，4，1： | 0 |
| 20 | Hierarchically penalized additive hazards model with diverging number of parameters显示文摘In many applications,covariates can be naturally grouped.For example,for gene expression data analysis,genes belonging to the same pathway might be viewed as a group.This paper studies variable selection problem for censored survival data in the additive hazards model when covariates are grouped.A hierarchical regularization method is proposed to simultaneously estimate parameters and select important variables at both the group level and the within-group level.For the situations in which the number of parameters tends to∞as the sample size increases,we establish an oracle property and asymptotic normality property of the proposed estimators.Numerical results indicate that the hierarchically penalized method performs better than some existing methods such as lasso,smoothly clipped absolute deviation(SCAD)and adaptive lasso. | LIU JiCai ZHANG RiQuan ZHAO WeiHua | 2014 | Science China Mathematics2014，57，4： | 0 |
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