刘小惠

个人简介情况表

姓名

刘小惠

性别

男

出生年月

1982-09

职务/职称

教授

学历/学位

博士

博导∕硕导

博导

所学专业

数理统计

电子邮箱

liuxiaohui@jxufe.edu.cn

学术研究领域：

稳健统计；半参数与非参数统计；时间序列分析；统计计算；混合效应模型

荣誉称号和社会团体兼职

江西省青年井冈学者

教 学 情 况

科 研 情 况

目前已完成学术论文90余篇，其中：

已经过同行评议的论文

[83] Peng, L., Liu, X. (通讯作者), Tan, X., Zhou, Y., Luo, S., (2023). The statistical rate for support matrix machines under low rankness and row (column) sparsity. Statistical Papers. 返修.

[82] Zhang, J., Fan, Y., Wang, Y., Liu, X., (2023). Towards a unified test for the intercept of autoregressive models. Journal of Applied Statistics. 返修.

[81] 邓子威,刘小惠(通讯作者),李园园,刘庆,朱恩文(2023). 一类基于Lq-范数的新型zonoid深度. 系统科学与数学. 返修.

[80] Liu, Q., Xia, C., Liu, X., (通讯作者) (2023). Limit theory for an AR(1) model with intercept and a possible infinite variance. Indian Journal of Pure and Applied Mathematics. DOI : 10.1007/s13226-023-00506-y.

[79] Zhu, E., Deng, Z., Zhang, H., Cao, J., Liu, X., (2023). Asymptotic inferences in the random coefficient autoregressive model with time-functional variance noises. 应用数学学报英文版. 返修.

[78] Tan, X., Peng, L., Xiao, P., Rizk, Z., Liu, X. (通讯作者), (2023). Expectile trace regression via low-rank and group sparsity regularization. Statistics. 录用.

[77] Li, C., Xiao, P., Ying, C., Liu, X. (通讯作者), (2023). Sliced Average Variance Estimation for Tensor Data. 应用数学学报英文版. 返修.

[76] Liu, Q., Zhang, J., Liu, X. (通讯作者), Hu, Z., 2023. Bahadur representations for the bootstrap median absolute deviation and the application to projection depth weighted mean. Metrika. Subject to revision.

[75] 刘小惠，范雅文，赵涵，范东慧，2023. 基于随机加权自助法的市场弱势有效性检验. 数理统计与管理. 录用.

[74] Chen, L., Liu, X., Peng, L., Zhu, F., 2023. Unified Inference for an Integer-valued AR(1) Model. Communications in Statistics - Theory and Methods. Subject to revision.

[73] Liu, X.(共同一作), Peng, L.(共同一作), Li, H., Liu, Y., 2023. Testing for serial correlation in predictive regression models without restricting the properties of predictors. Journal of Financial Econometrics. Subject to revision.

[72] Zhang, J., Li, B., Wang, Y., Liu, X. (通讯作者), Peng, P., 2023. Empirical likelihood unifies the stable and unstable integer-valued AR(1) models. Journal of Statistical Planning and Inference. 录用.

[71] Liu, X., Rizk, Z. (学生), Peng, L., Zhou, Y., Lian, H., 2022. Generalized Functional Additive Models in Reproducing Kernel Hilbert Spaces. Statistics and Its Inferface. Subject to revision.

[70] Rao Y., Fan, Y.(学生), Ao, H.,(学生), Liu., X. (通讯作者), 2022. A new portmanteau test for predictive regression models with possible embedded endogeneity. Journal of Time Series Analysis. Revised.

[69] Yang, B., Long, W., Liu, X., Peng, L., 2022. A Unified Predictability Test Using Weighted Inference And Random Weighted Bootstrap. Journal of Business & Economic Statistics. Revised.

[68] Luo, S., Wang, D., Dai, Y., Liu, X., 2023. Grated Recurrent Unit Network Quantile Regression for Silicon Content Prediction in Blast Furnace. ISIJ International. 63(11).

[67] Fan, Y.(学生), Liu, X., Cao, Y.(学生), Liu, S.(学生), 2023. Jackknife empirical likelihood based diagnostic checking for AR(p) models. Computational Statistics. https://doi.org/10.1007/s00180-023-01385-x.

[66] Liu, Y.(学生), Peng, L., Liu, Q., Lian, H., Liu, X.(通讯作者), 2023. Functional additive expectile regression in the reproducing kernel Hilbert space. Jouranl of Multivariate Analysis. Volume 198, November 2023, 105214.

[65] Liu, X., Chen, H., Ma, X., Yu, H., Yang, H., Ai L., Liu Q., Wu L., 2023. The retrospective data analysis on the spectrum of nervous system diseases in children. Scientific Reports. In press.

[64] Tan, X., Peng, L., Xiao, P., Liu, Q., Liu, X.(通讯作者), 2023. Optimal convergence rate for sparse and low-rank quantile trace regression. Journal of Complexity. Online first. https://www.sciencedirect.com/science/article/abs/pii/S0885064X2300047X.

[63] Fan, Y. (学生), Liu, X., Luo, T., Rao, Y., Li, H.(学生), 2023. Testing Serial Correlation in a General d-factor Model with Possible Infinite Variance. Journal of Applied Statistics. 10.1080/02664763.2023.2231175.

[62] Yang, B., Liu, X., Long, W., Peng, L., 2022. A Unified Unit Root Test Regardless of Intercept. Econometric Reviews. Accepted for Publication.

[61] Ding, J., Jiang, L., Liu, X., Peng, L. 2023. Nonparametric Tests For Market Timing Ability Using Daily Mutual Fund Returns. Journal of Economic Dynamics and Control. https://doi.org/10.1016/j.jedc.2023.104635.

[60] Liu, X., Long, W., Peng, L., Yang, B., 2023. A Unified Inference for Predictive Quantile Regression. Journal of the American Statistical Association. 已录用.

[59] 李传权,方岚然(学生),苏琦(学生),刘小惠,盛积良, 2023. 基于复杂网络的开源软件生态系统研究–以R软件为例, 系统科学与数学. 录用.

[58] Zhang, J., Li, B., Liu, X., Wan, X.(学生), 2023. Asymptotic behavior of the portmanteau tests in an integer-valued AR model. Journal of Nonparametric Statistics, https://doi.org/10.1080/10485252.2023.2175594.

[57] Wei, X.(学生), Liu, X., Fan, Y.(学生), Tan, L., Liu Q., 2022. A unified test for the AR error structure of an autoregressive model. Axioms. 11(12), 690, https://doi.org/10.3390/axioms11120690.

[56] Wang, Q. (学生), Liu, X., Fan, Y. (学生), Peng, L., 2022. Testing the Intercept of a Balanced Predictive Regression Model. Entropy, 2022, 24, 1594.

[55] Liu, Y. (学生), Liu, X., Pan, Y. (学生), Jiang, J., Xiao, P. (学生). An empirical comparison of various MSPE estimators and associated prediction intervals for small area means, Journal of Statistical Computation and Simulation, 10.1080/00949655. 2022.2144854.

[54] 李传权，马海强，刘小惠，刘育孜(学生), 2022. 基于Huber损失的稳健张量回归及其应用. 数理统计与管理. 录用.

[53] Liu, X., Fan, Y. (学生), Liu, Y. (学生), Luo,S., 2022. A Unit Root Test for an AR(1) Process with A Errors by Using Random Weighted Bootstrap. 数学学报, 录用.

[52] Liu, X., Fan, D. (学生), Zhang, X., Liu, C., 2022. Empirical likelihood-based portmanteau tests for autoregressive moving average models with possible infinite variance. Statistics and Its Interface. In Press.

[51] 程宏波, 钟文帆，陈艳华，李加加，刘小惠, 2022. 一种基于Lasso理论的牵引变电所接地网腐蚀诊断方法. 铁道学报, 录用.

[50] Li, H. (学生), Liu, X., Chen, Y. (学生), Fan, Y. (学生), 2022. Testing for serial correlation in autoregressive exogenous models with possible GARCH errors. Entropy. 24, 1076.

[49] Peng, L., Tan, X., Xiao, P.(学生), Rizk, Z.(学生), Liu, X.(通讯作者), 2022. Oracle inequality for sparse trace regression models with exponential beta-mixing errors. 数学学报英文版, 在线发表.

[48] 刘小惠，李园园(学生). 2022. 回归深度函数的一般概念. 应用数学学报. 2022, 5:461-482.

[47] Yang, Y., Fan, Y. (学生), Jiang, L., Liu, X. (通讯作者), 2022. Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors? Annals of Tourism Research. 93, 103365.

[46] Wang, L., Zhang, J., Li, B., Liu, X., 2022. Quantile trace regression via nuclear norm regularization. Statistics and Probability Letters, Volume 182, March 2022.

[45] Liu, X., Wang, L., Ma, X.(麻先思), Wang, J.(王婕雯), Wu, L., 2021. Modeling the effect of age on quantiles of the incubation period distribution of COVID-19. BMC Public Health, 21: 1762 (2021).

[44] 刘育孜(学生)，曲维荣，崔珍，刘小惠(通讯作者)，徐文婧(学生)，蒋继明, 2021. 基于分类混合效应模型预测方法和卫星遥感数据的农作物面积估算.数理统计与管理. 10.13860/j.cnki.sltj.20211130-027.

[43] 罗婷(学生)，刘小惠，程宏波. Elastic-Net变量选择及其在配网投资决策中的应用.电力系统保护与控制.49(23):1-8, 2021.

[42] Yang, G., Liu, X., Lian, H., 2021. Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces. Journal of Complexity. Volume 66, October 2021, 101568.

[41] Liu, X., Ma, H., Jiang, J., 2022. That Prasad-Rao is Robust: Estimation of Mean Squared Prediction Error of Observed Best Predictor under Potential Model Misspecification, Statistica Sinica, 32, 2217-2240.

[40] Liu, X., Lu, W., Lian, H., Liu, Y.(学生), Zhong, Z.Y. 2022, Partially Linear Additive Functional Regression, Statistica Sinica, 32, 2199-2216.

[39] Liu, X., Wang, Y.(学生), Fan, Y.(学生), Liu, Y.(学生), Statistical inferences for a partially linear model with autoregression errors, Acta Mathematicae Applicatae Sinica (English Series), 2022, 38:1-22.

[38] Liu, X., Li, Y., Jiang, J., Simple Measures of Uncertainty for Model Selection. Test, 2020, https://link.springer.com/article/10. 1007/s11749-020-00737-9.

[37] Liu, X., Ma, H., Jiang, J., Assessing Uncertainty in Small Area Estimation Involving Model Misspecification: A One-bring-one Route, 中国科学数学, 2020, 10.1007/s11425-020-1797-4.

[36] Liu, X., Liu, Y. (学生), Rao, Y., Lu, F., A unified test for the intercept of a predictive regression model, Oxford Bulletin of Economics and Statistics. 2021, 83: 571-588.

[35] Yuan, Y.F., Liu, X., Chen, Y. (学生), Dong, Y. (学生), Liu, Y.(学生), On similarity of the sample projection depth contours and its application. Communications in Statistics-Theory and Methods, DOI:10.1080/03610926.2020.1802651.

[34] Liu, X., Liu, Y. (学生), Lu, F., Empirical likelihood-based uniform confidence region for a predictive regression model, Communication in Statistics: Simulation and Computation, 2020+, 10.1080/03610918.2019.1670841.

[33] Yang, B., Liu, X., Peng, L., Cai, Z., Unified Tests for a Dynamic Predictive Regression, Journal of Business & Economic Statistics, 2021, 39: 684-699.

[32] Rahman, J.(学生), Luo, S., Fan, Y.(学生), Liu, X., Semiparametric Efficient Inferences for Generalised Partially Linear Models, Journal of Nonparametric Statistics. 2020, 32, 704-724.

[31] 刘小惠，何阳(学生)，麻先思(学生)，罗良清. 有关新冠肺炎潜伏期和疑似期的统计数据分析：基于湖北省外2172条确诊数据. 应用数学学报. 2020, 43: 278-294.

[30] Huang, H., Leng, X., Liu, X., Peng, L., Unified Inference for an AR Process with Possible Infinite Variance GARCH Errors, Journal of Financial Econometrics, 2020, 18, 425-470.

[29] Liu, X., Luo, S., Zuo, Y., Some results on the computing of Tukey’s halfspace median, Statistical papers, 2020, 61(1), 303-316.

[28] Liu, X., He, Y.(学生), RR-plot: A descriptive tool for regression observations, Journal of Applied Statistics, 2020, 47: 76-90.

[27] 范雅文(学生), 刘小惠, 预测回归模型序列相关性的Jackknife经验似然比检验. 系统科学与数学. 2019, 39: 1471-1485.

[26] Liu, X., Mosler, K., Mozharovskyi, P., Fast computation of Tukey trimmed regions and median in dimension p>2, Journal of Computational and Graphical Statistics, 2019, 28, 682-697.

[25] Liu, X., Rahman, J.(学生), Luo, S., Generalized and robustified empirical depths for multivariate data, Statistics and Probability Letters, 2019, 146, 70-76.

[24] Liu, X., Yang, B., Cai, Z., Peng, L., A Unified Test for Predictability of Asset Returns Regardless of Properties of Predicting Variables, Journal of Econometrics, 2019, 208, 141-159.

[23] Liu, X., Peng, L., Asymptotic Theory and Unified Confidence Region For An Autoregressive Model, Journal of Time Series Analysis, 2019, 40, 43-65.

[22] Liu, Y., Liu, X., Testing conditional independence with data missing at random. Applied Mathematics-A Journal of Chinese Universities, 2018, 33, 298-312.

[21] Liu Y., Wang, Q., Liu, X., Testing conditional independence via integrating-up transform, Statistics, 2018, 52, 734-749.

[20] Liu, X., Luo, S., Zuo, Y., The limit of finite sample breakdown point of Tukey’s halfspace median for general data, Acta Math Sinica(SCI), 2018, 34, 1403-1416.

[19] Liu, X., Zuo, Y., Wang, Q.H., Finite sample breakdown point of Tukey’s halfspace median. Science China Mathematics, 2017, 60, 861–874.

[18] Liu, X. (2017). Fast implementation of the Tukey depth. Computational Statistics, 32(4), 1395-1410.

[17] Liu, X., Approximating projection depth median in higher dimensions, Communication in Statistics: Simulation and Comput- ation, 2017, 46, 3756-3768.

[16] Liu, X., Wang, Q., Liu Y., A consistent jackknife empirical likelihood test for distribution functions, Annals of the Institute of Statistical Mathematics, 2017, 69, 249–269.

[15] Liu, X., Empirical likelihood for the response mean of generalized linear models with missing at random responses, Communication in Statistics: Simulation and Computation, 2017, 46, 164-173.

[14] Guo, W., Liu, X., Zhang, S., The Principal Correlation Components Estimator and its Optimality, Statistical Papers, 2016, 57, 755-779.

[13] Liu, X., Luo, S., A skewness-adaptive projection depth, Acta Mathematica Sinica, Chinese Series (数学学报中文版, 国内A), 2015, 58, 1-14.

[12] Liu, X., Yijun Zuo. CompPD: A MATLAB Package for Computing Projection Depth,Journal of Statistical Software(SCI 2017影响因子22.737), 2015, 65(2), 1-21.

[11] Liu, X., Ren, H., Wang, G., Computing halfspace depth contours based on the idea of a circular sequence, Journal of Systems Science and Complexity, 2015, 28, 1399-1411.

[10] Liu, X., Empirical likelihood-based inferences in varying coefficient models with missing data, Acta Mathematicae Applicatae Sinica, English Series, 2015, 31: 823-840

[9] Liu, X., Zuo, Y., Computing projection depth and its associated estimators, Statistics and Computing, 2014, 24: 51-63.

[8] Liu, X., Zuo, Y., Computing halfspace depth and regression depth. Communication in Statistics: Simulation and Computation, 2014, 43: 969-985.

[7] Liu, X., Zuo, Y., Wang, Z., Exactly computing bivariate projection depth contours and median, Computational Statistics and Data Analysis, 2013, 60: 1-11.

[6] Hu, X., Liu, X., Empirical likelihood confidence regions for semivarying coefficient models with linear process errors. Journal of Nonparametric Statistic, 2013, 25: 161-180.

[5] Wang, G., Wang, Z., Liu, X., Empirical likelihood for censored partial linear model based on imputed value. Communication in Statistics: Theory and Methods, 2013, 42: 644-659.

[4] Liu, X., Hu, X., Wang, G., Li, B., Zero finite-order serial correlation test in partially linear single index models, Journal of Systems Science and Complexity, 2012, 25(6): 1185-1201.

[3] Liu, X., Wang, Z., Hu, X., Estimation in partially linear single index models with missing covariates. Communication in Statistics: Theory and Methods, 2012, 41(18): 3428- 3447.

[2] Liu, X., Wang, Z., Hu, X., Testing heteroscedasticity in partially linear models with missing covariates. Journal of Nonparametric Statistics, 2011, 23 (2): 321-337.

[1] Liu, X., Wang, Z., Hu, X., Wang, G., Testing serial correlation in partially linear single-index errors-in-variables models. Communication in Statistics: Theory and Methods, 2011, 40 (14): 2554-2573.

指 导 学 生 情 况

本科生：

统计学院2012级拔尖班  班主任

统计学院2016级拔尖班  班主任

硕士生：

2015级 蒋亚飞(学硕，已毕业)

2016级 平珊珊 叶佳美 乔阳(学硕，已毕业) 李国栋(专硕，已毕业)

2017级 范雅文(已硕士毕业，并在读博士) 王嘉欣 吴也(来自非洲贝宁，已硕士毕业，并在读博士(中南大学))(学硕) 刘育孜(专硕，已硕士毕业，并在读博士) 李志勇(专硕，已毕业) 万琴(专硕，已毕业)

博士生(合作导师)：

Jafer Rahman(来自巴基斯坦，已毕业)

Zeinab Rizk(来自埃及)

学生团队：

所指导的学生共有3个学术小组：

1、经济计量模型的统计推断(侧重理论研究性工作)；

2、混合效应模型及其应用(侧重理论研究性工作)；

3、应用统计数据分析(侧重应用性工作，主要从事统计软件编程、电力数据分析、医学数据分析，工具语言为C++、R).

热烈欢迎各位有志于学习进取的同学积极加入我们团队！

其 他 情 况

项目情况：

先后主持国家自然科学基金-地区项目、青年项目、面上项目各一项；中国博士基金项目一等资助一项，特别资助一项；江西省自然科学基金青年项目、面上项目、青年重大项目各一项，江西省教育厅青年项目、重点项目各一项；第五批(2014年度)江西财经大学优秀青年术人才支持计划项目一项。

曾为下列杂志审稿人：

Communication in Statistics: Simulation and computation;

Communication in Statistics: Theory and Method;

Statistics and Probability Letters;

Computational Statistics and Data Analysis；

International Journal of Computer Mathematics;

Journal of Applied Statistics;

Open Journal of Statistics;

系统科学与数学;

Kybernetika-International journal published by Institute of Information Theory and Automation;

Statistical papers;

Annals of the Institute of Statistical Mathematics;

The Canadian Journal of Statistics

Journal of American Statistical Association

Journal of Statistical Computation and Simulation

数学物理学报英文版

中国科学数学

Statistica Sinica

Statistics and Its Interface

Statistics

Journal of the Korean Statistical Society