期刊： STATISTICAL SCIENCE, 2021; 36 (2)
Stochastic approximation was introduced in 1951 to provide a new theoretical framework for root finding and optimization of a regression function in t......
期刊： STATISTICAL SCIENCE, 2021; 36 (2)
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations. We develop a novel algorithm of pr......
期刊： STATISTICAL SCIENCE, 2020; 35 (1)
Quantum science and quantum technology are of great current interest in multiple frontiers of many scientific fields ranging from computer science to ......
期刊： STATISTICAL SCIENCE, 2020; 35 (1)
Unsupervised methods, including clustering methods, are essential to the analysis of single-cell genomic data. Model-based clustering methods are unde......
期刊： STATISTICAL SCIENCE, 2019; 34 (2)
This is a contribution to the discussion of the enlightening paper by Professor Efron. We focus on empirical Bayes interval estimation. We discuss the......
期刊： STATISTICAL SCIENCE, 2019; 34 (3)
Finite mixture models have offered a very important tool for exploring complex data structures in many scientific areas, such as economics, epidemiolo......
期刊： STATISTICAL SCIENCE, 2019; 34 (4)
We develop a model-free theory of general types of parametric regression for i.i.d. observations. The theory replaces the parameters of parametric mod......
期刊： STATISTICAL SCIENCE, 2019; 34 (4)
In the early 1980s, Halbert White inaugurated a "model-robust" form of statistical inference based on the "sandwich estimator" of standard error. This......
期刊： STATISTICAL SCIENCE, 2017; 32 (1)
The large-sample properties of likelihood-based statistical inference under mixture models have received much attention from statisticians. Although t......
期刊： STATISTICAL SCIENCE, 2017; 32 (3)
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper,......
