Xiaoqian Sun

Beijing University of Technology

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Min Wang

The University of Texas at San Antonio

Dongchu Sun

East China Normal University

Xian Zhou

Hainan Marine Monitoring and Forecasting Center

Jinhong You

Shanghai University of Finance and Economics

Tao Lu

University of Nevada, Reno

Zhuoqiong He

University of Missouri

Chanseok Park

Pusan National University

John M. Kabrick

Northern Research Station

Leping Liu

Third Xiangya Hospital

Daojiang He

Anhui Normal University

Cooperative Institutions

Beijing University of Technology

East China Normal University

Hong Kong Polytechnic University

University of Missouri

Chinese Academy of Sciences

Shanghai University

Michigan Technological University

Chinese Academy of Medical Sciences & Peking Union Medical College

Peking University

University of Nevada, Reno

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Bayesian Poisson Log-normal Model with Regularized Time Structure for Mortality Projection of Multi-population

arXiv (Cornell University) (2020)

Zhen Liu Xiaoqian Sun Leping Liu Yubo Wang

The improvement of mortality projection is a pivotal topic in the diverse branches related to insurance, demography, and public policy. Motivated by the thread of Lee-Carter related models, we propose a Bayesian model to estimate and predict mortality rates for multi-population. This new model features in information borrowing among populations and properly reflecting variations of data. It also provides a solution to a long-time overlooked problem: model selection for dependence structures of population-specific time parameters. By introducing a Dirac spike function, simultaneous model selection and estimation for population-specific time effects can be achieved without much extra computation cost. We use the Japanese mortality data from Human Mortality Database to illustrate the desirable properties of our model.

Approximate Bayesian Computation

Population model

10.48550/arxiv.2010.04775

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Citations (2)

Bayesian analysis of Birnbaum–Saunders distribution via the generalized ratio-of-uniforms method

Computational Statistics (2015)

Min Wang Xiaoqian Sun Chanseok Park

10.1007/s00180-015-0629-z

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Citations (31)

Bayesian analysis of two-piece location–scale models under reference priors with partial information

Computational Statistics & Data Analysis (2015)

Shiyi Tu Min Wang Xiaoqian Sun

Conjugate prior

10.1016/j.csda.2015.11.002

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Citations (3)

Jackknifing type weighted least squares estimators in partially linear regression models

Statistics & Probability Letters (2002)

Jinhong You Xiaoqian Sun Wan-Kai Pang Ping-kei Leung

Jackknife resampling

10.1016/s0167-7152(02)00242-0

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Citations (0)

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

Computational Statistics & Data Analysis (2008)

Xiaoqian Sun Zhuoqiong He John M. Kabrick

10.1016/j.csda.2007.12.019

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Citations (8)

Bayes factor consistency for unbalanced ANOVA models

Statistics (2013)

Min Wang Xiaoqian Sun

In practical situations, most experimental designs often yield unbalanced data which have different numbers of observations per unit because of cost constraints, missing data, etc. In this paper, we consider the Bayesian approach to hypothesis testing or model selection under the one-way unbalanced fixed-effects analysis-of-variance (ANOVA) model. We adopt Zellner's g-prior with the beta-prime distribution for g, which results in an explicit closed-form expression of the Bayes factor without integral representation. Furthermore, we investigate the model selection consistency of the Bayes factor under three different asymptotic scenarios: either the number of units goes to infinity, the number of observations per unit goes to infinity, or both go to infinity. The results presented extend some existing ones of the Bayes factor for the balanced ANOVA models in the literature.

Infinity

10.1080/02331888.2012.694445

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Citations (13)

Estimation of the multivariate normal precision and covariance matrices in a star-shape model

Annals of the Institute of Statistical Mathematics (2005)

Dongchu Sun Xiaoqian Sun

Equivariant map

Invariant estimator

Matrix (chemical analysis)

Bayes estimator

10.1007/bf02509235

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Citations (19)

Objective Bayesian hypothesis testing and estimation for the intraclass model

Statistical Theory and Related Fields (2018)

Duo Zhang Daojiang He Xiaoqian Sun Tao Lu Min Wang

The intraclass correlation coefficient (ICC) plays an important role in various fields of study as a coefficient of reliability. In this paper, we consider objective Bayesian analysis for the ICC in the context of normal linear regression model. We first derive two objective priors for the unknown parameters and show that both result in proper posterior distributions. Within a Bayesian decision-theoretic framework, we then propose an objective Bayesian solution to the problems of hypothesis testing and point estimation of the ICC based on a combined use of the intrinsic discrepancy loss function and objective priors. The proposed solution has an appealing invariance property under one-to-one reparametrisation of the quantity of interest. Simulation studies are conducted to investigate the performance the proposed solution. Finally, a real data application is provided for illustrative purposes.

Point estimation

Bayes estimator

10.1080/24754269.2018.1481586

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Citations (1)

Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project

Xiaoqian Sun

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.

File format

10.32469/10355/4477

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Citations (2)

Statistical inference for partially linear additive spatial autoregressive models

Spatial Statistics (2018)

Jiang Du Xiaoqian Sun Ruiyuan Cao Zhongzhan Zhang

Statistical Inference

Additive model

Asymptotic Analysis

10.1016/j.spasta.2018.04.008

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Citations (65)