Yuan Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Yuan Sun has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Chi Zhang
College of Economics Shenzhen University Shenzhen Guangdong 518055 People's Republic of China
2 shared publications - Guo-Liang Tian
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, P. R. China
2 shared publications - Guo-Liang Tian
Department of Mathematics, Southern University of Science and Technology, Shenzhen, Guangdong, China
1 shared publication - Li Tao
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
1 shared publication - Lianjie Shu
Faculty of Business Administration, University of Macau
1 shared publication - Man-Lai Tang
Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong
1 shared publication
Latest Publications
- Open Access
- 24 January 2022
- Published by MDPI AG in Mathematics
- Vol. 10 (3) , 353
Bivariate Continuous Negatively Correlated Proportional Models with Applications in Schizophrenia Research
Bivariate continuous negatively correlated proportional data defined in the unit square often appear in many different disciplines, such as medical studies, clinical trials and so on. To model this type of data, the paper proposes two new bivariate continuous distributions (i.e., negatively correlated proportional inverse Gaussian (NPIG) and negatively correlated proportional gamma (NPGA) distributions) for the first time and provides corresponding distributional properties. Two mean regression models are further developed for data with covariates. The normalized expectation–maximization (N-EM) algorithm and the gradient descent algorithm are combined to obtain the maximum likelihood estimates of parameters of interest. Simulations studies are conducted, and a data set of cortical thickness for schizophrenia is used to illustrate the proposed methods. According to our analysis between patients and controls of cortical thickness in typical mutual inhibitory brain regions, we verified the compensatory of cortical thickness in patients with schizophrenia and found its negative correlation with age.- 24 January 2022
- Published by MDPI AG in Mathematics
- Vol. 10 (3) , 353
Open AccessBivariate Continuous Negatively Correlated Proportional Models with Applications in Schizophrenia Research
Bivariate continuous negatively correlated proportional data defined in the unit square often appear in many different disciplines, such as medical studies, clinical trials and so on. To model this type of data, the paper proposes two new bivariate continuous distributions (i.e., negatively correlated proportional inverse Gaussian (NPIG) and negatively correlated proportional gamma (NPGA) distributions) for the first time and provides corresponding distributional properties. Two mean regression models are further developed for data with covariates. The normalized expectation–maximization (N-EM) algorithm and the gradient descent algorithm are combined to obtain the maximum likelihood estimates of parameters of interest. Simulations studies are conducted, and a data set of cortical thickness for schizophrenia is used to illustrate the proposed methods. According to our analysis between patients and controls of cortical thickness in typical mutual inhibitory brain regions, we verified the compensatory of cortical thickness in patients with schizophrenia and found its negative correlation with age. - 5 September 2021
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 93 (6) , 956-982
Likelihood-based methods for the zero-one-two inflated Poisson model with applications to biomedicine
To model count data with excess zeros, ones and twos, for the first time we introduce a so-called zero-one-two-inflated Poisson (ZOTIP) distribution, including the zero-inflated Poisson (ZIP) and the zero-and-one-inflated Poisson (ZOIP) distributions as two special cases. We establish three equivalent stochastic representations for the ZOTIP random variable to develop important distributional properties of the ZOTIP distribution. The Fisher scoring and expectation–maximization (EM) algorithms are derived to obtain the maximum likelihood estimates of parameters of interest. Bootstrap confidence intervals are also provided. Testing hypotheses are considered, simulation studies are conducted, and two real data sets are used to illustrate the proposed methods.
- 5 September 2021
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 93 (6) , 956-982
Likelihood-based methods for the zero-one-two inflated Poisson model with applications to biomedicine
To model count data with excess zeros, ones and twos, for the first time we introduce a so-called zero-one-two-inflated Poisson (ZOTIP) distribution, including the zero-inflated Poisson (ZIP) and the zero-and-one-inflated Poisson (ZOIP) distributions as two special cases. We establish three equivalent stochastic representations for the ZOTIP random variable to develop important distributional properties of the ZOTIP distribution. The Fisher scoring and expectation–maximization (EM) algorithms are derived to obtain the maximum likelihood estimates of parameters of interest. Bootstrap confidence intervals are also provided. Testing hypotheses are considered, simulation studies are conducted, and two real data sets are used to illustrate the proposed methods.
- 3 April 2021
- Published by Taylor & Francis in Crystallography Reviews
- Vol. 27 (2) , 124-126
Introduction to crystallography
I have a confession: although I have read biographies of crystallographers, this is the first book on crystallography that I have read from cover to cover. I am one of those crystallographers who h...
- 3 April 2021
- Published by Taylor & Francis in Crystallography Reviews
- Vol. 27 (2) , 124-126
Introduction to crystallography
I have a confession: although I have read biographies of crystallographers, this is the first book on crystallography that I have read from cover to cover. I am one of those crystallographers who h...
- 1 May 2020
- Published by Science China Press., Co. Ltd. in SCIENTIA SINICA Mathematica
- Vol. 50 (5) , 711
A new multivariate Laplace distribution based on the mixture of normal distributions
In this paper, we propose a new multivariate Laplace distribution from normal variance mixture models, called as Type II multivariate Laplace distribution. Unlike the original multivariate Laplace distribution whose all components must have the same value for the mixing variate, the random components in the new distribution could have different value for its own mixing variate and are correlated only through the dependence structure of the normal random vector. Thus, it contains the multiplication of independently identical distribution univariate Laplace distributions as a special case if the normal covariance matrix is diagonal. A tractable stochastic representation is used to derive the probability density function and other statistical properties. The maximum likelihood estimates of parameters via an ECM (expectation/conditional maximization) algorithm and the Bayesian methods are derived. Some simulation studies are conducted to evaluate the performance of the proposed methods. Applications in two real data sets indicate that the Type II multivariate Laplace distribution could have a better performance and is distinct from the original one.
- 1 May 2020
- Published by Science China Press., Co. Ltd. in SCIENTIA SINICA Mathematica
- Vol. 50 (5) , 711
A new multivariate Laplace distribution based on the mixture of normal distributions
In this paper, we propose a new multivariate Laplace distribution from normal variance mixture models, called as Type II multivariate Laplace distribution. Unlike the original multivariate Laplace distribution whose all components must have the same value for the mixing variate, the random components in the new distribution could have different value for its own mixing variate and are correlated only through the dependence structure of the normal random vector. Thus, it contains the multiplication of independently identical distribution univariate Laplace distributions as a special case if the normal covariance matrix is diagonal. A tractable stochastic representation is used to derive the probability density function and other statistical properties. The maximum likelihood estimates of parameters via an ECM (expectation/conditional maximization) algorithm and the Bayesian methods are derived. Some simulation studies are conducted to evaluate the performance of the proposed methods. Applications in two real data sets indicate that the Type II multivariate Laplace distribution could have a better performance and is distinct from the original one.