4 citations · 9 across the 15 of their papers we have counts for
15 papers
Discretization approximation: An alternative to Monte Carlo in Bayesian computation
Shifeng Xiong
In this paper we propose a new deterministic approximation method, called discretization approximation, for Bayesian computation. Discretization approximation is very simple to und…
Nonparametric inference with massive data via grouped empirical likelihood
Yongda Wang, Shifeng Xiong
To address the computational issue in empirical likelihood methods with massive data, this paper proposes a grouped empirical likelihood (GEL) method. It divides observations i…
A unified framework of principal component analysis and factor analysis
Shifeng Xiong
Principal component analysis and factor analysis are fundamental multivariate analysis methods. In this paper a unified framework to connect them is introduced. Under a general lat…
Unweighted estimation based on optimal sample under measurement constraints
Jing Wang, HaiYing Wang, Shifeng Xiong
To tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsa…
Physical Parameter Calibration
Yang Li, Shifeng Xiong
Computer simulation models are widely used to study complex physical systems. A related fundamental topic is the inverse problem, also called calibration, which aims at learning ab…
Design and analysis of computer experiments with both numeral and distribution inputs
Chunya Li, Xiaojun Cui, Shifeng Xiong
Nowadays stochastic computer simulations with both numeral and distribution inputs are widely used to mimic complex systems which contain a great deal of uncertainty. This paper st…
Linear screening for high-dimensional computer experiments
Chunya Li, Daijun Chen, Shifeng Xiong
In this paper we propose a linear variable screening method for computer experiments when the number of input variables is larger than the number of runs. This method uses a linear…
The Reconstruction Approach: From Interpolation to Regression
Shifeng Xiong
This paper introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible e…
Personalized Optimization for Computer Experiments with Environmental Inputs
Shifeng Xiong
Optimization problems with both control variables and environmental variables arise in many fields. This paper introduces a framework of personalized optimization to han- dle such…
Local optimization-based statistical inference
Shifeng Xiong
This paper introduces a local optimization-based approach to test statistical hypotheses and to construct confidence intervals. This approach can be viewed as an extension of boots…
Comparisons of penalized least squares methods by simulations
Ke Zhang, Fan Yin, Shifeng Xiong
Penalized least squares methods are commonly used for simultaneous estimation and variable selection in high-dimensional linear models. In this paper we compare several prevailing…
Better Solution Principle: A Facet of Concordance between Optimization and Statistics
Shifeng Xiong
Many statistical methods require solutions to optimization problems. When the global solution is hard to attain, statisticians always use the better if there are two solutions for…
Better subset regression
Shifeng Xiong
To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a spar…
On best subset regression
Shifeng Xiong
In this paper we discuss the variable selection method from \ell0-norm constrained regression, which is equivalent to the problem of finding the best subset of a fixed size. Our st…
OEM for least squares problems
Shifeng Xiong, Bin Dai, Peter Z. G. Qian
We propose an algorithm, called OEM (a.k.a. orthogonalizing EM), intended for var- ious least squares problems. The first step, named active orthogonization, orthogonalizes an arbi…