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学者姓名：李高荣
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摘要 ：
The penalized variable selection methods are often used to select the relevant covariates and estimate the unknown regression coefficients simultaneously, but these existing methods may fail to be consistent for the setting with highly correlated covariates. In this paper, the semi-standard partial covariance (SPAC) method with Lasso penalty is proposed to study the generalized linear model with highly correlated covariates, and the consistencies of the estimation and variable selection are shown in high-dimensional settings under some regularity conditions. Some simulation studies and an analysis of colon tumor dataset are carried out to show that the proposed method performs better in addressing highly correlated problem than the traditional penalized variable selection methods.
关键词 ：
Generalized linear model Generalized linear model variable selection variable selection highly correlated covariates highly correlated covariates semi-standard partial covariance semi-standard partial covariance Lasso penalty Lasso penalty
引用：
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| GB/T 7714 | Yue, Li Li , Wang, Wei Tao , Li, Gao Rong . Variable Selection for Generalized Linear Model with Highly Correlated Covariates [J]. | ACTA MATHEMATICA SINICA-ENGLISH SERIES , 2024 , 40 (6) : 1458-1480 . |
| MLA | Yue, Li Li 等. "Variable Selection for Generalized Linear Model with Highly Correlated Covariates" . | ACTA MATHEMATICA SINICA-ENGLISH SERIES 40 . 6 (2024) : 1458-1480 . |
| APA | Yue, Li Li , Wang, Wei Tao , Li, Gao Rong . Variable Selection for Generalized Linear Model with Highly Correlated Covariates . | ACTA MATHEMATICA SINICA-ENGLISH SERIES , 2024 , 40 (6) , 1458-1480 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we study the tests for sphericity and identity of covariance matrices in time-varying coefficient high-dimensional panel data models with fixed effects. In order to construct the effective test statistics and avoid the influence of the unknown fixed effects, we apply the difference method to eliminate the dependence of the residual sample, and further construct test statistics using the trace estimators of the covariance matrices. For the estimators of the coefficient functions, we use the local linear dummy variable method. Under some regularity conditions, we study the asymptotic property of the estimators and establish the asymptotic distributions of our proposed test statistics without specifying an explicit relationship between the cross-sectional and the time series dimensions. We further show that the test statistics are asymptotic distribution-free. Subsequently simulation studies are carried out to evaluate our proposed methods. In order to assess the performance of our proposed test method, we compare with the existing test methods in panel data linear models with fixed effects.
关键词 ：
Sphericity test Sphericity test Time-varying coefficient model Time-varying coefficient model Fixed effects Fixed effects Identity test Identity test Panel data Panel data
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| GB/T 7714 | Chen, Ranran , Li, Gaorong , Feng, Sanying . Testing for covariance matrices in time-varying coefficient panel data models with fixed effects [J]. | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2020 , 49 (1) : 82-116 . |
| MLA | Chen, Ranran 等. "Testing for covariance matrices in time-varying coefficient panel data models with fixed effects" . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY 49 . 1 (2020) : 82-116 . |
| APA | Chen, Ranran , Li, Gaorong , Feng, Sanying . Testing for covariance matrices in time-varying coefficient panel data models with fixed effects . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2020 , 49 (1) , 82-116 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we simultaneously study variable selection and estimation problems for sparse ultra-high dimensional partially linear varying coefficient models, where the number of variables in linear part can grow much faster than the sample size while many coefficients are zeros and the dimension of nonparametric part is fixed. We apply the B-spline basis to approximate each coefficient function. First, we demonstrate the convergence rates as well as asymptotic normality of the linear coefficients for the oracle estimator when the nonzero components are known in advance. Then, we propose a nonconvex penalized estimator and derive its oracle property under mild conditions. Furthermore, we address issues of numerical implementation and of data adaptive choice of the tuning parameters. Some Monte Carlo simulations and an application to a breast cancer data set are provided to corroborate our theoretical findings in finite samples.
关键词 ：
High dimensionality High dimensionality Nonconvex penalty Nonconvex penalty Oracle property Oracle property Partially linear varying coefficient model Partially linear varying coefficient model Variable selection Variable selection
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| GB/T 7714 | Wang, Zhaoliang , Xue, Liugen , Li, Gaorong et al. Spline estimator for ultra-high dimensional partially linear varying coefficient models [J]. | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS , 2019 , 71 (3) : 657-677 . |
| MLA | Wang, Zhaoliang et al. "Spline estimator for ultra-high dimensional partially linear varying coefficient models" . | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 71 . 3 (2019) : 657-677 . |
| APA | Wang, Zhaoliang , Xue, Liugen , Li, Gaorong , Lu, Fei . Spline estimator for ultra-high dimensional partially linear varying coefficient models . | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS , 2019 , 71 (3) , 657-677 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Randomized experiment is an important tool for studying the Average Treatment Effect (ATE). This paper considers the regression adjustment estimation of the Sample Average Treatment Effect (SATE) in high-dimensional case, where the multicollinearity problem is often encountered and needs to be properly handled. Many existing regression adjustment methods fail to achieve satisfactory performances. To solve this issue, an Elastic-net adjusted estimator for SATE is proposed under the Rubin causal model of randomized experiments with multicollinearity in high dimensions. The asymptotic properties of the proposed SATE estimator are shown under some regularity conditions, and the asymptotic variance is proved to be not greater than that of the unadjusted estimator. Furthermore, Neyman-type conservative estimators for the asymptotic variance are proposed, which yields tighter confidence intervals than both the unadjusted and the Lasso-based adjusted estimators. Some simulation studies are carried out to show that the Elastic-net adjusted method is better in addressing collinearity problem than the existing methods. The advantages of our proposed method are also shown in analyzing the dataset of HER2 breast cancer patients. (C) 2018 Elsevier B.V. All rights reserved.
关键词 ：
Randomized experiments Randomized experiments Elastic-net Elastic-net Rubin causal model Rubin causal model Causal inference Causal inference Average Treatment Effect Average Treatment Effect High-dimensional data High-dimensional data
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| GB/T 7714 | Yue, Lili , Li, Gaorong , Lian, Heng et al. Regression adjustment for treatment effect with multicollinearity in high dimensions [J]. | COMPUTATIONAL STATISTICS & DATA ANALYSIS , 2019 , 134 : 17-35 . |
| MLA | Yue, Lili et al. "Regression adjustment for treatment effect with multicollinearity in high dimensions" . | COMPUTATIONAL STATISTICS & DATA ANALYSIS 134 (2019) : 17-35 . |
| APA | Yue, Lili , Li, Gaorong , Lian, Heng , Wan, Xiang . Regression adjustment for treatment effect with multicollinearity in high dimensions . | COMPUTATIONAL STATISTICS & DATA ANALYSIS , 2019 , 134 , 17-35 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we consider the estimation problem of quantile varying-coefficient models when the link function is unspecified, which significantly expands the existing works on varying-coefficient models with unspecified link function focusing only on mean regression. We provide new identification conditions which are weaker than existing ones. Under these identification conditions, we use polynomial splines to estimate both the varying coefficients and the link functions and establish the convergence rate of the estimator. Our simulation studies and a real data application illustrate the finite sample performance of the estimators.
关键词 ：
Asymptotic property Asymptotic property B-splines B-splines Check loss minimization Check loss minimization Quantile regression Quantile regression Single-index models Single-index models
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| GB/T 7714 | Yue, Lili , Li, Gaorong , Lian, Heng . Identification and estimation in quantile varying-coefficient models with unknown link function [J]. | TEST , 2019 , 28 (4) : 1251-1275 . |
| MLA | Yue, Lili et al. "Identification and estimation in quantile varying-coefficient models with unknown link function" . | TEST 28 . 4 (2019) : 1251-1275 . |
| APA | Yue, Lili , Li, Gaorong , Lian, Heng . Identification and estimation in quantile varying-coefficient models with unknown link function . | TEST , 2019 , 28 (4) , 1251-1275 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we propose a nonparametric independence screening method for sparse ultra-high dimensional generalized varying coefficient models with longitudinal data. Our methods combine the ideas of sure independence screening (SIS) in sparse ultrahigh dimensional generalized linear models and varying coefficient models with the marginal generalized estimating equation (GEE) method, called NIS-GEE, considering both the marginal correlation between response and covariates, and the subject correlation for variable screening. The corresponding iterative algorithm is introduced to enhance the performance of the proposed NIS-GEE method. Furthermore it is shown that, under some regularity conditions, the proposed NIS-GEE method enjoys the sure screening properties. Simulation studies and a real data analysis are used to assess the performance of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.
关键词 ：
Generalized estimating equation Generalized estimating equation Generalized varying coefficient model Generalized varying coefficient model Nonparametric independence screening Nonparametric independence screening Sure screening properties Sure screening properties Ultra-high longitudinal data Ultra-high longitudinal data
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| GB/T 7714 | Zhang, Shen , Zhao, Peixin , Li, Gaorong et al. Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data [J]. | JOURNAL OF MULTIVARIATE ANALYSIS , 2019 , 171 : 37-52 . |
| MLA | Zhang, Shen et al. "Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data" . | JOURNAL OF MULTIVARIATE ANALYSIS 171 (2019) : 37-52 . |
| APA | Zhang, Shen , Zhao, Peixin , Li, Gaorong , Xu, Wangli . Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data . | JOURNAL OF MULTIVARIATE ANALYSIS , 2019 , 171 , 37-52 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we consider the single-index measurement error model with mismeasured covariates in the nonparametric part. To solve the problem, we develop a simulation-extrapolation (SIMEX) algorithm based on the local linear smoother and the estimating equation. For the proposed SIMEX estimation, it is not needed to assume the distribution of the unobserved covariate. We transform the boundary of a unit ball in Rp to the interior of a unit ball in Rp-1 by using the constraint =1. The proposed SIMEX estimator of the index parameter is shown to be asymptotically normal under some regularity conditions. We also derive the asymptotic bias and variance of the estimator of the unknown link function. Finally, the performance of the proposed method is examined by simulation studies and is illustrated by a real data example.
关键词 ：
Measurement error Measurement error Estimating equation Estimating equation Single-index model Single-index model SIMEX SIMEX Local linear smoother Local linear smoother
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| GB/T 7714 | Yang, Yiping , Tong, Tiejun , Li, Gaorong . SIMEX estimation for single-index model with covariate measurement error [J]. | ASTA-ADVANCES IN STATISTICAL ANALYSIS , 2019 , 103 (1) : 137-161 . |
| MLA | Yang, Yiping et al. "SIMEX estimation for single-index model with covariate measurement error" . | ASTA-ADVANCES IN STATISTICAL ANALYSIS 103 . 1 (2019) : 137-161 . |
| APA | Yang, Yiping , Tong, Tiejun , Li, Gaorong . SIMEX estimation for single-index model with covariate measurement error . | ASTA-ADVANCES IN STATISTICAL ANALYSIS , 2019 , 103 (1) , 137-161 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
考虑超高维部分线性变系数模型,其中线性部分的协变量的维数随着样本容量以指数阶的速度增长.考虑到超高维协变量间存在相关性,提出贪婪的profile向前回归(greedy profile forward regression,GPFR)方法对超高维的线性部分的协变量进行变量筛选.并在一定的正则条件下,证明了所提出GPFR方法的筛选相合性.GPFR方法得到一系列嵌套的模型,为确定是否将某个候选的解释变量选入模型,用EBIC准则选择"最优"的模型.通过数值模拟和实例分析研究了GPFR算法的有限样本性质,发现在变量间存在高度相关和信噪比较低时,所提的GPFR方法优势明显.
关键词 ：
超高维 超高维 变量筛选 变量筛选 向前回归 向前回归 部分线性变系数模型 部分线性变系数模型 筛选相合性 筛选相合性
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| GB/T 7714 | 李玉杰 , 李高荣 . 超高维部分线性变系数模型的贪婪变量筛选 [J]. | 北京工业大学学报 , 2018 , 44 (09) : 1247-1256 . |
| MLA | 李玉杰 et al. "超高维部分线性变系数模型的贪婪变量筛选" . | 北京工业大学学报 44 . 09 (2018) : 1247-1256 . |
| APA | 李玉杰 , 李高荣 . 超高维部分线性变系数模型的贪婪变量筛选 . | 北京工业大学学报 , 2018 , 44 (09) , 1247-1256 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for the plausibility of this application. In this paper, we develop procedures to test whether or not the parametric production frontier functions are suitable. Toward this aim, we developed two test statistics based on local smoothing and an empirical process, respectively. Residual-based wild bootstrap versions of these two test statistics are also suggested. The distributions of technical inefficiency and the noise term are not specified, which allows specification testing of the production frontier function even under heteroscedasticity. Simulation studies and a real data example are presented to examine the finite sample sizes and powers of the test statistics. The theory developed in this paper is useful for production managers in their decisions on production.
关键词 ：
smoothing process smoothing process production frontier function production frontier function specification testing specification testing stochastic frontier model stochastic frontier model simulations simulations wild bootstrap wild bootstrap empirical process empirical process
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| GB/T 7714 | Guo, Xu , Li, Gao-Rong , McAleer, Michael et al. Specification Testing of Production in a Stochastic Frontier Model [J]. | SUSTAINABILITY , 2018 , 10 (9) . |
| MLA | Guo, Xu et al. "Specification Testing of Production in a Stochastic Frontier Model" . | SUSTAINABILITY 10 . 9 (2018) . |
| APA | Guo, Xu , Li, Gao-Rong , McAleer, Michael , Wong, Wing-Keung . Specification Testing of Production in a Stochastic Frontier Model . | SUSTAINABILITY , 2018 , 10 (9) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Noisy and missing data are often encountered in real applications such that the observed covariates contain measurement errors. Despite the rapid progress of model selection with contaminated covariates in high dimensions, methodology that enjoys virtues in all aspects of prediction, variable selection, and computation remains largely unexplored. In this paper, we propose a new method called as the balanced estimation for high-dimensional error-in-variables regression to achieve an ideal balance between prediction and variable selection under both additive and multiplicative measurement errors. It combines the strengths of the nearest positive semi-definite projection and the combined L-1 and concave regularization, and thus can be efficiently solved through the coordinate optimization algorithm. We also provide theoretical guarantees for the proposed methodology by establishing the oracle prediction and estimation error bounds equivalent to those for Lasso with the clean data set, as well as an explicit and asymptotically vanishing bound on the false sign rate that controls overfitting, a serious problem under measurement errors. Our numerical studies show that the amelioration of variable selection will in turn improve the prediction and estimation performance under measurement errors. (C) 2018 Elsevier B.V. All rights reserved.
关键词 ：
Balanced estimation Balanced estimation Model selection Model selection Measurement errors Measurement errors Combined L-1 and concave regularization Combined L-1 and concave regularization High dimensionality High dimensionality Nearest positive semi-definite projection Nearest positive semi-definite projection
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| GB/T 7714 | Zheng, Zemin , Li, Yang , Yu, Chongxiu et al. Balanced estimation for high-dimensional measurement error models [J]. | COMPUTATIONAL STATISTICS & DATA ANALYSIS , 2018 , 126 : 78-91 . |
| MLA | Zheng, Zemin et al. "Balanced estimation for high-dimensional measurement error models" . | COMPUTATIONAL STATISTICS & DATA ANALYSIS 126 (2018) : 78-91 . |
| APA | Zheng, Zemin , Li, Yang , Yu, Chongxiu , Li, Gaorong . Balanced estimation for high-dimensional measurement error models . | COMPUTATIONAL STATISTICS & DATA ANALYSIS , 2018 , 126 , 78-91 . |
| 导入链接 | NoteExpress RIS BibTex |
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