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学者姓名：李高荣
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Abstract ：
投资组合在金融领域扮演着重要的角色,其中最经典的是均值方差模型的最优投资组合.文章针对"尖峰厚尾"或者异方差等金融数据,提出了 Lasso惩罚分位数回归方法研究最优投资组合问题.在一定正则条件下,证明了所提方法得到的结果接近于给定的风险值,且渐近达到了最大预期收益.模拟研究和实证研究通过风险和夏普比率两个指标,对所提方法进行了评价,并和其他投资组合方法进行了比较,充分说明了所提方法的稳健性和有效性.
Keyword ：
均值方差投资组合 均值方差投资组合 Lasso Lasso 夏普比率 夏普比率 分位数回归 分位数回归
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| GB/T 7714 | 曹梦娜 , 田萍 , 李高荣 . 最优投资组合的Lasso惩罚分位数回归研究 [J]. | 系统科学与数学 , 2021 , 41 (09) : 2595-2611 . |
| MLA | 曹梦娜 等. "最优投资组合的Lasso惩罚分位数回归研究" . | 系统科学与数学 41 . 09 (2021) : 2595-2611 . |
| APA | 曹梦娜 , 田萍 , 李高荣 . 最优投资组合的Lasso惩罚分位数回归研究 . | 系统科学与数学 , 2021 , 41 (09) , 2595-2611 . |
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Abstract ：
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.
Keyword ：
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 . |
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Abstract ：
本文研究了面板数据交互固定效应模型中协方差矩阵的检验问题.首先依据模型协方差矩阵迹的估计构造检验统计量,检验协方差矩阵是否为单位矩阵,或是单位矩阵的常数倍.然后在一定正则条件下,证明了检验统计量的渐近性质,并说明所提出的检验方法不依赖于误差分布.最后,通过模拟研究对本文的检验方法进行评价,说明所提检验方法在高维面板数据下仍然有效.
Keyword ：
交互固定效应模型 交互固定效应模型 协方差矩阵 协方差矩阵 Frobenius范数 Frobenius范数 高维面板数据 高维面板数据
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| GB/T 7714 | 陈冉冉 , 李高荣 . 面板数据交互固定效应模型的协方差矩阵检验 [J]. | 应用概率统计 , 2019 , 35 (06) : 621-638 . |
| MLA | 陈冉冉 等. "面板数据交互固定效应模型的协方差矩阵检验" . | 应用概率统计 35 . 06 (2019) : 621-638 . |
| APA | 陈冉冉 , 李高荣 . 面板数据交互固定效应模型的协方差矩阵检验 . | 应用概率统计 , 2019 , 35 (06) , 621-638 . |
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Abstract ：
本文研究参数单指标时间序列分位数自回归模型有效性的检验问题.当分位数回归变量的维数较大时,现有的检验方法将面临"维数灾难"问题.为了解决这个问题,本文基于残差经验过程,利用降维思想构造统计量,它有效地适应于参数单指标时间序列分位数自回归模型.本文提出Khmaladze鞅转换方法来替代经验过程,并构造检验统计量,证明所构造的检验统计量能够渐近收敛到分布自由的标准Brown运动.模拟研究和实际数据分析的结果表明,本文所提方法在参数单指标分位数自回归模型的检验中优于已有的检验方法.
Keyword ：
单指标时间序列 单指标时间序列 模型检验 模型检验 分位数回归 分位数回归 渐近分布自由 渐近分布自由 降维 降维 残差经验过程 残差经验过程 Khmaladze转换 Khmaladze转换
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| GB/T 7714 | 夏强 , 梁茹冰 , 李高荣 . 参数单指标分位数自回归模型的诊断检验 [J]. | 中国科学:数学 , 2019 , 49 (06) : 879-898 . |
| MLA | 夏强 等. "参数单指标分位数自回归模型的诊断检验" . | 中国科学:数学 49 . 06 (2019) : 879-898 . |
| APA | 夏强 , 梁茹冰 , 李高荣 . 参数单指标分位数自回归模型的诊断检验 . | 中国科学:数学 , 2019 , 49 (06) , 879-898 . |
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Abstract ：
We consider a problem of checking whether the coefficient of the scale and location function is a constant. Both the scale and location functions are modeled as single-index models. Two test statistics based on Kolmogorov-Smirnov and Cramer-von Mises type functionals of the difference of the empirical residual processes are proposed. The asymptotic distribution of the estimator for single-index parameter is derived, and the empirical distribution function of residuals is shown to converge to a Gaussian process. Moreover, the proposed test statistics can be able to detect local alternatives that converge to zero at a parametric convergence rate. A bootstrap procedure is further proposed to calculate critical values. Simulation studies and a real data analysis are conducted to demonstrate the performance of the proposed methods.
Keyword ：
Empirical residual process Empirical residual process single-index models single-index models local linear smoothing local linear smoothing
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| GB/T 7714 | Zhang, Jun , Niu, Cuizhen , Li, Gaorong . Exploring the constant coefficient of a single-index variation [J]. | BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS , 2019 , 33 (1) : 57-86 . |
| MLA | Zhang, Jun 等. "Exploring the constant coefficient of a single-index variation" . | BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS 33 . 1 (2019) : 57-86 . |
| APA | Zhang, Jun , Niu, Cuizhen , Li, Gaorong . Exploring the constant coefficient of a single-index variation . | BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS , 2019 , 33 (1) , 57-86 . |
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Abstract ：
Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this article, we provide theoretical foundations on the power and robustness for the model-X knockoffs procedure introduced recently in Candes, Fan, Janson and Lv in high-dimensional setting when the covariate distribution is characterized by Gaussian graphical model. We establish that under mild regularity conditions, the power of the oracle knockoffs procedure with known covariate distribution in high-dimensional linear models is asymptotically one as sample size goes to infinity. When moving away from the ideal case, we suggest the modified model-X knockoffs method called graphical nonlinear knockoffs (RANK) to accommodate the unknown covariate distribution. We provide theoretical justifications on the robustness of our modified procedure by showing that the false discovery rate (FDR) is asymptotically controlled at the target level and the power is asymptotically one with the estimated covariate distribution. To the best of our knowledge, this is the first formal theoretical result on the power for the knockoffs procedure. Simulation results demonstrate that compared to existing approaches, our method performs competitively in both FDR control and power. A real dataset is analyzed to further assess the performance of the suggested knockoffs procedure. for this article are available online.
Keyword ：
Power Power Big data Big data Large-scale inference and FDR Large-scale inference and FDR Graphical nonlinear knockoffs Graphical nonlinear knockoffs Reproducibility Reproducibility High-dimensional nonlinear models High-dimensional nonlinear models Robustness Robustness
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| GB/T 7714 | Fan, Yingying , Demirkaya, Emre , Li, Gaorong et al. RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs [J]. | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2019 , 115 (529) : 362-379 . |
| MLA | Fan, Yingying et al. "RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs" . | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 115 . 529 (2019) : 362-379 . |
| APA | Fan, Yingying , Demirkaya, Emre , Li, Gaorong , Lv, Jinchi . RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs . | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2019 , 115 (529) , 362-379 . |
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Abstract ：
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.
Keyword ：
Sure screening properties Sure screening properties Nonparametric independence screening Nonparametric independence screening Ultra-high longitudinal data Ultra-high longitudinal data Generalized estimating equation Generalized estimating equation Generalized varying coefficient model Generalized varying coefficient model
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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 . |
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Abstract ：
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.
Keyword ：
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 . |
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Abstract ：
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.
Keyword ：
Variable selection Variable selection Oracle property Oracle property Nonconvex penalty Nonconvex penalty Partially linear varying coefficient model Partially linear varying coefficient model High dimensionality High dimensionality
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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 . |
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Abstract ：
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.
Keyword ：
Quantile regression Quantile regression B-splines B-splines Asymptotic property Asymptotic property Single-index models Single-index models Check loss minimization Check loss minimization
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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 . |
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