Office : 105 Probability and Statistics Office, Wenli Building
Introduction
Research
Teaching
Funding
Publications
Awards
Honor Reward
Admission
Introduction
Name: Li Zhiqiang Gender: Male
Education and degree: Doctoral student, Major: Non parametric Statistics, Title: Associate Professor
Education and Main Work Experience
March 2019 -present Teacher at the Department of Mathematics, School of Mathematics and Physics, Beijing University of Chemical Technology
June 2001-March 2019 Teacher, Mathematics Department, School of Science, Beijing University of Chemical Technology
July 1993-July 1998 Teacher at Baoding No.15 Middle School
September 2004-July 2007, School of Applied Mathematics and Physics, Beijing University of Technology, majoring in Applied Mathematical Statistics, with a PhD in Science and supervised by Professor Xue Liugen
September 1998-May 2001, School of Science, Northern Jiaotong University, majoring in Applied Stochastic Analysis, with a Master's degree in Science and supervised by Professor Liu Kunhui
September 1994-July 1997, Department of Mathematics, Hebei University, majoring in Applied Mathematics, Bachelor of Science
September 1991-June 1993 Baoding Normal College, majoring in Mathematics
Expert in the evaluation of the National Graduate Mathematical Modeling Competition, expert in the master's evaluation of the Ministry of Education's expert database, member of the Big Data Statistics Branch of the China Association for On site Statistics, and member of the China Society of Industrial and Applied Mathematics. Guided all undergraduate and graduate students in the school to participate in mathematical modeling competitions and won more than thirty second and third prizes. Led two horizontal projects and guided more than ten graduate students in the field of applied mathematical statistics. Published more than thirty academic papers in core journals and SCI journals.
Research direction
Inference of Non parametric and Semi parametric Statistical Models
Missing data statistical analysis
Statistical analysis of longitudinal data models
Bayesian statistical modeling of small sample data
Statistical diagnosis and testing of experimental data
Quantitative structure-activity relationship modeling of chemical experimental data
Algorithm research on statistical models under massive data
Research on Robust Statistical Analysis Algorithm Based on Quantile Regression
Introduction to research content
The research areas mainly involve non parametric statistics, longitudinal data statistical analysis, Bayesian statistical analysis, model statistical inference algorithms under massive data, quantile regression, and diagnostic testing of regression models.
Education
Work Experience
Social Position
Social Activities
Research
The research areas mainly involve non parametric statistics, longitudinal data statistical analysis, Bayesian statistical analysis, model statistical inference algorithms under massive data, quantile regression, and diagnostic testing of regression models.
·Engaged in research on semi parametric statistics under missing data, and achieved certain research results in the estimation theory of partial linear models. In terms of methodology, a series of methods such as restricted maximum likelihood, Profile least squares, EM algorithm, non parametric smoothing, and cross-sectional kernel estimation were mainly used to conduct detailed and in-depth research on partial linear models and partial linear variable coefficient models with non negligible dropouts under missing data. Currently, we have comprehensively understood the history and current situation of the research field of this project, mastered the statistical inference methods and related algorithms of semi parametric models, variable coefficient models, mixed effects models, and longitudinal data.
·Conduct statistical modeling and quantitative structure-activity relationship research on ultra small sample data, screen important influencing indicator variables, and conduct factor analysis. The proposed two-step variable selection method can effectively overcome the problems of multicollinearity and overfitting in small sample data. The problem of change points encountered in experimental data can be solved through methods such as threshold regression.
·Based on the data obtained from orthogonal experimental design, a longitudinal data mixing effect model is used to model and find the optimal chemical reaction conditions. By using quantile regression estimation methods and diagnostic tests of models to perform robust statistical analysis on data, strong influence points and possible outlier data points can be identified.
·Statistical modeling for massive data can effectively reduce computational burden and improve computational efficiency by utilizing methods such as smooth approximation of functions and divide and conquer algorithms to achieve computer parallel computing.
·A method based on model and estimation formula correction is used to eliminate the influence of measurement errors in the data and find the correct model relationship between indicator variables.
·Using machine learning and statistical modeling methods to characterize some stoichiometric problems, such as data classification, indicator prediction, material uniformity, outlier testing, etc., non parametric modeling and testing methods are used to effectively evaluate experimental conclusions.
Teaching
The undergraduate and graduate courses taught include，
Probability Theory and Mathematical Statistics,
Time Series Analysis,
Fundamentals of Information Theory,
Introduction to Mathematics，
Multivariate statistical analysis，
Applied Mathematical Statistics,
Advanced Mathematical Statistics,
Postgraduates
Funding
Establishment Method of Small Sample Indicative Parameter Model to Serve Unreliant Project Research Achievements on August 30, 2022 December 31, 2022
Research on Small Sample Modeling and Statistical Inference Based on Bayesian Framework Other Topics on October 15, 2020 and October 14, 2022
Quantitative structure-activity relationship and statistical inference research on catalytic reaction data, self selected topic 2019-11-01, 2021-10-31
Nie Haowei and Li Zhiqiang. Smooth quantile regression aggregation estimation under massive data。 journal paper statistics and decision-making， November 10, 2023
Li Hongkai, Xiao Songtao, Ouyang Yinggen, and Li Zhiqiang. Quantile estimation of linear mixed effects models. Journal of Beijing University of Chemical Technology (Natural Science Edition), May 20, 2021
Wang Rui, Zhou Rongxi, Li Zhiqiang. Forecast the Term Structure of treasury bond Interest Rate Based on Dynamic SV Model Journal of Beijing University of Chemical Technology, August 20, 2020
Chen Shaodong and Li Zhiqiang. Research on Aggregation Estimation Algorithms for Generalized Linear Model Parameters under Massive Data. Journal Paper Statistics and Information Forum, 2020-07-25
Chen Shaodong and Li Zhiqiang. Research on Aggregation Estimation Algorithms for Generalized Linear Model Parameters under Massive Data Journal Paper Statistics and Information Forum, July 10, 2020
Chen Shaodong, Li Zhiqiang. Research on Variable Selection Algorithms for Generalized Linear Models with Massive Data. Journal of Beijing University of Chemical Technology, April 15, 2020
Geng Qiao, Li Zhiqiang,Chen Shaodong. Estimation Algorithm for Linear Mixing Effect Models under Massive Data. Journal Paper of Beijing University of Chemical Technology (Natural Science Edition) 2019-05-20
Sun Yu, Li Zhiqiang. Bayesian Analysis of Factors Influencing Cash Dividend Payment Intention of Chinese A-share Listed Companies Based on Generalized Partial Linear Mixed Model. Journal of Beijing University of Chemical Technology (Natural Science Edition) 2018-05-20
Li Jie, Li Zhiqiang, Liu Xiao, and Yan Bailu. correlation analysis between genetic loci and disease information based on neural networks and particle swarm optimization algorithm. Journal of Beijing University of Chemical Technology (Natural Science Edition) 2018-01-20
Zhong Jinpan, Li Zhiqiang, Guan Weijiang, Lv Chao. Cation - π Interaction Triggered Fluence of Clay Fillers in Polymer Compositions for Quantification of Three Dimensional Machinery. Journal Paper ANALYTICAL CHEMISTRY 2017-10-28
Yuan Chaojie, Sun Yu, and Li Zhiqiang. A Study on the Factors Influencing the Profitability of Real Estate Listed Companies Based on a Semi parametric Partial Linear Mixed Effect Model. Journal of Beijing University of Chemical Technology, Natural Science Edition, 2017-07-20
Li Zhiqiang. Explores the Training Mode of Innovative Ability of Grades in the Platform of Graded Mathematical Modeling Competition Conference Paper 2016 International Conference on Education, Science and Education Management (ESEM 2016) 2016-10-20
Hu Chengsheng, Li Zhiqiang, Wang Qianying. Effect Testing of Partial Linear EV Panel Data Model with Variable Coefficients. Journal of Beijing University of Chemical Technology, Natural Science Edition, 2016-03-20
Wang Qianying, Li Zhiqiang, Hu Chengsheng. Bootstrap Hausman test for individual effects in partial linear panel data models. Journal of Beijing University of Chemical Technology Journal of Natural Science, 2016-01-20
Li Zhiqiang, Xue, Liu Gen. Generalized Partial Linear Models with Missing Data. Journal of Beijing University of Chemical Technology (Natural Science Edition) 2006-08-01
