Tsung-I Lin

发表

Automated high-dimensional flow cytometric data analysis

J. Mesirov, G. McLachlan, P. Tamayo, 2009, Proceedings of the National Academy of Sciences.

The 2nd Special issue on Mixture Models

S. Ingrassia, Tsung-I Lin, 2022, Econometrics and Statistics.

Heavy-tailed longitudinal regression models for censored data: a robust parametric approach

V. H. Lachos, Tsung-I Lin, L. M. Castro, 2018, TEST.

Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution

Tsung-I Lin, Tsung-I Lin, Jack C. Lee, 2007 .

Heavy-tailed longitudinal regression models for censored data: A likelihood based perspective

V. H. Lachos, Tsung-I Lin, L. M. Castro, 2015 .

Editorial for the 4th Special Issue on advances in mixture models

John Hinde, Paul D. McNicholas, Tsung-I Lin, 2019, Comput. Stat. Data Anal..

A robust factor analysis model based on the canonical fundamental skew-t distribution

Tsung-I Lin, Wan-Lun Wang, I-An Chen, 2022, Statistical Papers.

Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies

Tsung-I Lin, Wan-Lun Wang, 2022, Biometrical journal. Biometrische Zeitschrift.

Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights

Tsung-I Lin, Wan-Lun Wang, Yu-Chen Yang, 2022, Advances in Data Analysis and Classification.

On the exact distribution of order statistics arising from a doubly truncated bivariate elliptical distribution

A. Jamalizadeh, Tsung-I Lin, Mehdi Amiri, 2018 .

A robust factor analysis model using the restricted skew-$$t$$t distribution

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2015 .

Maximum likelihood estimation for scale-shape mixtures of flexible generalized skew normal distributions via selection representation

A. Jamalizadeh, Tsung-I Lin, Abbas Mahdavi, 2021, Computational Statistics.

Shape mixtures of skew-t-normal distributions: characterizations and estimation

A. Jamalizadeh, Tsung-I Lin, M. Tamandi, 2018, Computational Statistics.

Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables

Tsung-I Lin, L. M. Castro, Wan-Lun Wang, 2020, Statistical Papers.

Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions

A. Jamalizadeh, Tsung-I Lin, Wan-Lun Wang, 2018, Statistical Papers.

A general class of scale-shape mixtures of skew-normal distributions: properties and estimation

A. Jamalizadeh, Tsung-I Lin, 2016, Computational Statistics.

Model-based clustering via mixtures of unrestricted skew normal factor analyzers with complete and incomplete data

Tsung-I Lin, Wan-Lun Wang, 2022, Statistical Methods & Applications.

Robust mixture regression modeling based on the normal mean-variance mixture distributions

Tsung-I Lin, Wan-Lun Wang, Mehrdad Naderi, 2022, Computational Statistics & Data Analysis.

Multivariate measurement error models with normal mean‐variance mixture distributions

Tsung-I Lin, Wan-Lun Wang, Mehrdad Naderi, 2022, Stat.

Flexible mixture modelling using the multivariate skew-t-normal distribution

Tsung-I Lin, Hsiu J. Ho, Chia-Rong Lee, 2013, Statistics and Computing.

Robust skew-t factor analysis models for handling missing data

Tsung-I Lin, Wan-Lun Wang, Min Liu, 2017, Statistical Methods & Applications.

Bayesian analysis of the p-order integer-valued AR process with zero-inflated Poisson innovations

Aldo M. Garay, Francyelle L. Medina, Tsung-I Lin, 2020 .

Robust clustering of multiply censored data via mixtures of t factor analyzers

Tsung-I Lin, Wan-Lun Wang, 2021, TEST.

Mixtures of restricted skew-t factor analyzers with common factor loadings

Tsung-I Lin, L. M. Castro, Wan-Lun Wang, 2018, Advances in Data Analysis and Classification.

Robust model-based clustering via mixtures of skew-t distributions with missing information

Tsung-I Lin, Wan-Lun Wang, 2015, Advances in Data Analysis and Classification.

Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2020, Advances in Data Analysis and Classification.

On moments of folded and truncated multivariate Student-t distributions based on recurrence relations

C. E. Galarza, V. H. Lachos, Tsung-I Lin, 2021, Metrika.

Flexible modeling of multiple nonlinear longitudinal trajectories with censored and non-ignorable missing outcomes.

Tsung-I Lin, Wan-Lun Wang, 2023, Statistical methods in medical research.

Automated learning of mixtures of factor analysis models with missing information

Tsung-I Lin, Wan-Lun Wang, 2020, TEST.

Extending finite mixtures of t linear mixed-effects models with concomitant covariates

Wan-Lun Wang, Tsung-I Lin, Luis M. Castro, 2020, Comput. Stat. Data Anal..

Robust clustering of multiply censored data via mixtures of t factor analyzers

Wan-Lun Wang, Tsung-I Lin, Tsung-I Lin, 2021, TEST.

Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables

Tsung-I Lin, L. M. Castro, Wan-Lun Wang, 2020 .

Model-based clustering of censored data via mixtures of factor analyzers

Wan-Lun Wang, Tsung-I Lin, Victor H. Lachos, 2019, Comput. Stat. Data Anal..

Mixtures of restricted skew-t factor analyzers with common factor loadings

Wan-Lun Wang, Tsung-I Lin, Luis M. Castro, 2018, Advances in Data Analysis and Classification.

A novel mixture model using the multivariate normal mean-variance mixture of Birnbaum-Saunders distributions and its application to extrasolar planets

Tsung-I Lin, A. Jamalizadeh, Wen-Liang Hung, 2019, J. Multivar. Anal..

Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2018 .

Flexible clustering via extended mixtures of common t-factor analyzers

Wan-Lun Wang, Tsung-I Lin, Tsung-I Lin, 2017 .

Robust model-based clustering via mixtures of skew-t distributions with missing information

Wan-Lun Wang, Tsung-I Lin, Tsung-I Lin, 2015, Advances in Data Analysis and Classification.

Maximum likelihood estimation for multivariate skew normal mixture models

Tsung I. Lin, Tsung-I Lin, 2009, J. Multivar. Anal..

Longitudinal Data Analysis Using t Linear Mixed Models with Autoregressive Dependence Structures

Tsung-I Lin, 2008 .

Bayesian analysis of Box–Cox transformed linear mixed models with ARMA(p,q) dependence

Tsung-I Lin, Tsung-I Lin, Jack C. Lee, 2005 .

On modelling data from degradation sample paths over time

Tsung-I Lin, Jack C. Lee, 2003 .

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

Geoffrey J. McLachlan, Sharon X. Lee, Tsung I. Lin, 2013, J. Multivar. Anal..

A robust approach to joint modeling of mean and scale covariance for longitudinal data

Tsung-I Lin, Yun-Jen Wang, 2009 .

Robust mixture modeling using the skew t distribution

Jack C. Lee, Tsung I. Lin, Wan J. Hsieh, 2007, Stat. Comput..

A Skew-Normal Mixture Regression Model

Tsung-I Lin, Tsung-I Lin, Min Liu, 2014 .

Some results on the truncated multivariate t distribution

Tsung-I Lin, Wan-Lun Wang, Hsiu J. Ho, 2012 .

Estimation and prediction in linear mixed models with skew‐normal random effects for longitudinal data

Tsung-I Lin, Jack C. Lee, 2008, Statistics in medicine.

Evaluating Risk Measures Using the Normal Mean-Variance Birnbaum-Saunders Distribution

Tsung-I Lin, Ahad Jamalizadeh, Mehrdad Naderi, 2020 .

Shape mixtures of skew-t-normal distributions: characterizations and estimation

Tsung-I Lin, Mostafa Tamandi, Ahad Jamalizadeh, 2018, Comput. Stat..

Multivariate t semiparametric mixed-effects model for longitudinal data with multiple characteristics

Tsung-I Lin, M. Taavoni, M. Arashi, 2020 .

On moments of folded and truncated multivariate Student-t distributions based on recurrence relations

C. E. Galarza, V. H. Lachos, Tsung-I Lin, 2021 .

Robust mixture modeling using multivariate skew t distributions

Tsung I. Lin, Tsung-I Lin, 2010, Stat. Comput..

Extending multivariate-t linear mixed models for multiple longitudinal data with censored responses and heavy tails

V. H. Lachos, Tsung-I Lin, Wan-Lun Wang, 2018, Statistical methods in medical research.

Multivariate skew-normal at linear mixed models for multi-outcome longitudinal data

Tsung-I Lin, Wan-Lun Wang, 2013 .

Analysis of multivariate skew normal models with incomplete data

Hsiu J. Ho, Tsung I. Lin, Chiang L. Chen, 2009, J. Multivar. Anal..

Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition

Tsung-I Lin, Tsung-I Lin, 2014, Comput. Stat. Data Anal..

Bayesian analysis of mixture modelling using the multivariate t distribution

Jack C. Lee, Tsung I. Lin, Huey F. Ni, 2004, Stat. Comput..

A skew factor analysis model based on the normal mean–variance mixture of Birnbaum–Saunders distribution

A. Jamalizadeh, Tsung-I Lin, Mehrdad Naderi, 2020, Journal of applied statistics.

Automated learning of t factor analysis models with complete and incomplete data

Wan-Lun Wang, Tsung-I Lin, Luis M. Castro, 2017, J. Multivar. Anal..

Robust skew-t factor analysis models for handling missing data

Min Liu, Wan-Lun Wang, Tsung-I Lin, 2017, Stat. Methods Appl..

Supervised learning of multivariate skew normal mixture models with missing information

Tsung-I Lin, Tzy-Chy Lin, Tsung-I Lin, 2010, Comput. Stat..

A robust approach to t linear mixed models applied to multiple sclerosis data

Tsung I Lin, Jack C Lee, Tsung-I Lin, 2006, Statistics in medicine.

An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers

Wan-Lun Wang, Tsung-I Lin, Tsung-I Lin, 2012, Computational Statistics.

Capturing patterns via parsimonious t mixture models

Paul D. McNicholas, Tsung-I Lin, Hsiu J. Ho, 2013, 1303.2316.

Robust statistical modelling using the multivariate skew t distribution with complete and incomplete data

Tsung-I Lin, Tzy-Chy Lin, 2011 .

Finite mixture modelling using the skew normal distribution

Tsung-I Lin, Jack C. Lee, shu-yun Yen, 2007 .

Maximum likelihood estimation for multivariate skew normal mixture models

Tsung-I Lin, 2009, J. Multivar. Anal..

A Multivariate Flexible Skew-Symmetric-Normal Distribution: Scale-Shape Mixtures and Parameter Estimation via Selection Representation

Tsung-I Lin, A. Jamalizadeh, Abbas Mahdavi, 2021, Symmetry.

Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2020, Adv. Data Anal. Classif..

Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions

A. Jamalizadeh, Tsung-I Lin, Wan-Lun Wang, 2018, Statistical Papers.

Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2018, 1802.02467.

On the exact distribution of order statistics arising from a doubly truncated bivariate elliptical distribution

A. Jamalizadeh, Tsung-I Lin, Mehdi Amiri, 2017, METRON.

Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

Tsung-I Lin, Hsiu J. Ho, Saumyadipta Pyne, 2012, Stat. Comput..

Multivariate t nonlinear mixed‐effects models for multi‐outcome longitudinal data with missing values

Tsung-I Lin, Wan-Lun Wang, 2014, Statistics in medicine.

Automated learning of mixtures of factor analysis models with missing information

Wan-Lun Wang, Tsung-I Lin, Tsung-I Lin, 2020 .

Flexible mixture modelling using the multivariate skew-t-normal distribution

Tsung-I Lin, Hsiu J. Ho, Chia-Rong Lee, 2014, Stat. Comput..

Computationally efficient learning of multivariate t mixture models with missing information

Tsung-I Lin, Hsiu J. Ho, Pao S. Shen, 2009, Comput. Stat..

Robust linear mixed models using the skew t distribution with application to schizophrenia data

Tsung-I Lin, Hsiu J. Ho, 2010, Biometrical journal. Biometrische Zeitschrift.

Bayesian analysis of multivariate t linear mixed models with missing responses at random

Tsung-I Lin, Wan-Lun Wang, 2015 .

The skew-t factor analysis model

Sharon X. Lee, G. McLachlan, Tsung-I Lin, 2013, 1310.5336.

Robust clustering via mixtures of t factor analyzers with incomplete data

Wan-Lun Wang, Tsung-I Lin, Tsung-I Lin, 2021, Advances in Data Analysis and Classification.

Flexible clustering via extended mixtures of common t-factor analyzers

Tsung-I Lin, Wan-Lun Wang, 2016, AStA Advances in Statistical Analysis.

Parametric modeling of cellular state transitions as measured with flow cytometry

Sui Huang, Steven B. Haase, Saumyadipta Pyne, 2011, BMC Bioinformatics.

On fast supervised learning for normal mixture models with missing information

Hsiu J. Ho, Jack C. Lee, Tsung I. Lin, 2006, Pattern Recognit..