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Journal of Data Science创刊于2003年。创刊主编为台湾中研院赵民德博士和台湾辅仁大学谢邦昌博士。本刊历年来发表的研究成果涉及领域广泛，始终致力于推动数据科学方法在各领域的应用。从2019年起，主办权由中国人民大学统计学院和教育部人文社科重点研究基地应用统计科学研究中心承担。为进一步提高学术期刊办刊水平，涵养学术品牌，作为中国人民大学于2019年启动“学术期刊质量提升计划（2...
Journal of Data Science创刊于2003年。创刊主编为台湾中研院赵民德博士和台湾辅仁大学谢邦昌博士。本刊历年来发表的研究成果涉及领域广泛，始终致力于推动数据科学方法在各领域的应用。从2019年起，主办权由中国人民大学统计学院和教育部人文社科重点研究基地应用统计科学研究中心承担。为进一步提高学术期刊办刊水平，涵养学术品牌，作为中国人民大学于2019年启动“学术期刊质量提升计划（2...
Journal of Data Science第21卷第3期已正式上线。Journal of Data Science创刊于2003年。创刊主编为台湾中研院赵民德博士和台湾辅仁大学谢邦昌博士。本刊历年来发表的研究成果涉及领域广泛，始终致力于推动数据科学方法在各领域的应用。从2019年起，主办权由中国人民大学统计学院和教育部人文社科重点研究基地应用统计科学研究中心承担。为进一步提高学术期刊办刊水平，...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Subsampling for Rare Events Data and maximum sampled conditional likelihood
罕见事件数据 子采样 最大采样 条件似然
2023/5/15
In this talk, we show that the available information about unknown parameters in rare events data is only tied to the relatively small number of cases, which justifies the usage of negative sampling. ...
中国人民大学统计学院吕晓玲教授、王菲菲副教授就联合文本主题建模问题在《Data Mining and Knowledge Discovery》发表论文（图）
吕晓玲 王菲菲 建模 文本
2024/11/26
中国人民大学统计学院吕晓玲教授、王菲菲副教授在《Data Mining and Knowledge Discovery》发表论文。该研究主要探讨了两种带有联动作用的文本主题建模问题。研究中提出了一种全新的Joint Dynamic Topic Model(JDTM)模型，该模型在传统的动态主题模型的基础上，通过建立两种文本在时间上的前后关联性，可以挖掘两类文本的“超前-滞后”作用（lead-lag...
Journal of Data Science第19卷第2期已正式上线，本期论文如下：
Direct Regression Modelling of High-order Moments in Big Data
Big data Higher-order moment U-statistics Estimating equation Divide-and-conquer
2016/1/26
Big data problems present great challenges to statisti-cal analyses, especially from the computational side. In this paper, we consider regression estimation of high-order mo-ments in big data problem...
Evaluation of somatic copy number estimation tools for whole-exome sequencing data
CNV prediction Somatic alterations The Cancer Genome Atlas CNV algorithms
2016/1/26
Evaluation of somatic copy number estimation tools for whole-exome sequencing data.
Estimating Spatial Autocorrelation with Sampled Network Data
NetworkDataAnalysis Paired Maximum Likelihood Estimator
2016/1/26
Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation re...
Bias Correction for Fixed Effects Spatial Panel Data Models
Bootstrap Spatial Panel Individual Fixed Effects Time Fixed Effects
2016/1/26
This paper examines the finite sample properties of the quasi maximum likelihood (QML) esti-mators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general b...
High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data
Generalized empirical likelihood High dimensionality Penalized likelihood Variable selec- tion
2016/1/26
This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the di...
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Feature Screening Pearson’s Chi-Square Test Screening Consisten- cy Search Engine Marketing
2016/1/26
Ultrahigh dimensional data with both categorical responses and categorical covari-ates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable s...
Spatial Panel Data Models. Oxford Handbook of Panel Data
Spatial Panel Data Model Oxford Handbook Panel Data
2016/1/26
The consideration of interactions among regions or agents has become increasingly important in various fields of economics. In public economics, a state government’s cigarettes tax rate will be influe...
Efficient GMM Estimation of Spatial Dynamic Panel Data Models
Spatial autoregression Dynamic panels Fixed effects Generalized method of moment
2016/1/26
In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with fixed effects when n is large, and T can be large, but small relative to n. The GMM es...
Estimation of Spatial Panel Data Models with Time Varying Spatial Weights Matrices
Spatial autoregression Panel data Time varying spatial weights matrices Fixed e¤ects Maximum likelihood Impact analysis
2016/1/26
This paper investigates the quasi-maximum likelihood (QML) estimation of spatial panel data models where spatial weights matrices can be time varying. We show that QML estimate is consistent and asymp...