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学者姓名：李高荣
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摘要 ：
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.
关键词 ：
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 . |
| 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 ：
Fixed effects Fixed effects Identity test Identity test Panel data Panel data Sphericity test Sphericity test Time-varying coefficient model Time-varying coefficient model
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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 ：
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.
关键词 ：
Khmaladze转换 Khmaladze转换 分位数回归 分位数回归 单指标时间序列 单指标时间序列 模型检验 模型检验 残差经验过程 残差经验过程 渐近分布自由 渐近分布自由 降维 降维
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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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
Frobenius范数 Frobenius范数 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 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 . |
| 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 等. "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 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 ：
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 ：
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 ：
Average Treatment Effect Average Treatment Effect Causal inference Causal inference Elastic-net Elastic-net High-dimensional data High-dimensional data Randomized experiments Randomized experiments Rubin causal model Rubin causal model
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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 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 ：
Estimating equation Estimating equation Local linear smoother Local linear smoother Measurement error Measurement error SIMEX SIMEX Single-index model Single-index model
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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 . |
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