Organizer and Chair: Professor Tsung-I Lin Affiliation: Institute of Statistics, National Chung Hsing University, Taiwan Email: tilin@nchu.edu.tw In this proposal, four speakers including myself are invited to present their recent extraordinary works related to innovative statistical modelling for complex data in the 64th ISI World Statistics Congress - Ottawa, Canada (July 16-20, 2023). The information of the four speakers is delivered below. The 1st presenter: Professor Victor Hugo Lachos Davila Affiliation: Department of Statistics, University of Connecticut, Storrs, USA Email: hlachos@uconn.edu Presenting Title: Likelihood-based inference for the multivariate skew-t regression with censored or missing responses The 2nd presenter: Professor Wan-Lun Wang Affiliation: Department of Statistics and Institute of Data Science, National Cheng Kung University, Taiwan Email: wangwl@gs.ncku.edu.tw Presenting Title: A selection approach to multivariate linear mixed models with censored and non-ignorable missing responses The 3rd presenter: Professor Luis Mauricio Castro Affiliation: Department of Statistics, Pontificia Universidad Católica de Chile, Chile Email: mcastro@mat.uc.cl Presenting Title: Using joint random partition models for flexible change point analysis in multivariate processes The 4th presenter: Professor Tsung-I Lin Affiliation: Institute of Statistics, National Chung Hsing University, Taiwan Email: tilin@nchu.edu.tw Presenting Title: Flexible clustering for asymmetric data via mixtures of unrestricted skew normal factor analyzers Here are the abstracts: The 1st presenter: Professor Victor Hugo Lachos Davila Title: Likelihood-based inference for the multivariate skew-t regression with censored or missing responses Abstract: Skew-t regression models have been widely used to model and analyze asymmetric heavy-tailed data. Moreover, observations in this kind of data can be missing or subject to some upper and/or lower detection limits because of the restriction of the experimental apparatus. For such data structures, we propose a novel robust regression model for multiply censored or missing data based on the multivariate skew-t distribution. This approach allows us to model data with great flexibility, simultaneously accommodating heavy tails and skewness. We develop an analytically simple, yet efficient, EM-type algorithm to conduct maximum likelihood estimation of the parameters. The algorithm has closed-form expressions at the E-step that rely on formulas for the mean and variance of truncated multivariate Student's-t, skew-t, and extended skew-t distributions. Furthermore, a general information-based method for approximating the asymptotic covariance matrix of the estimators is also presented. Results obtained from the analysis of both simulated and real datasets are reported to demonstrate the effectiveness of the proposed method. The 2nd presenter: Professor Wan-Lun Wang Title: A selection approach to multivariate linear mixed models with censored and non-ignorable missing responses Abstract: The analysis of multivariate longitudinal data could encounter some complications due to censorship induced by detection limits of the assay and non-response occurring when participants missed scheduled visits intermittently or discontinued participation. This paper establishes a generalization of the multivariate linear mixed model that can accommodate censored responses and non-ignorable missing outcomes simultaneously. To account for the non-ignorable missingness, the selection approach which decomposes the joint distribution as a marginal distribution for the primary outcome variables and a model describing the missing process conditional on the hypothetical complete data is used. A computationally feasible Monte Carlo expectation conditional maximization (MCECM) algorithm is developed for parameter estimation with the maximum likelihood (ML) method. Furthermore, a general information-based approach is presented to assess the variability of ML estimators. The techniques for the prediction of censored responses and imputation of missing outcomes are also discussed. The methodology is motivated and exemplified by a real dataset concerning HIV-AIDS clinical trials. A simulation study is conducted to examine the performance of the proposed method compared with other traditional approaches. The 3rd presenter: Professor Luis Mauricio Castro Title: Using joint random partition models for flexible change point analysis in multivariate processes Abstract: Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underlying distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of information. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our approach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies. The 4th presenter: Professor Tsung-I Lin Title: Flexible clustering for asymmetric data via mixtures of unrestricted skew normal factor analyzers Abstract: Mixtures of factor analyzers (MFA) based on the restricted skew normal distribution (rMSN) has been shown to be a flexible tool to handle asymmetrical high-dimensional data with heterogeneity. However, the rMSN distribution is oft-criticized a lack of sufficient ability to accommodate potential skewness arisen from more than one feature space. This paper presents an alternative extension of MFA by assuming the unrestricted skew normal (uMSN) distribution for the component factors. In particular, the proposed mixtures of unrestricted skew normal factor analyzers (MuSNFA) can simultaneously capture multiple directions of skewness and deal with the occurrence of missing values or nonresponses. Under the missing at random (MAR) mechanism, we develop a computationally feasible expectation conditional maximization (ECM) algorithm for computing the maximum likelihood estimates of model parameters. Practical aspects related to model-based clustering, prediction of factor scores and missing values are also discussed. The utility of the proposed methodology is illustrated with the analysis of simulated data and the Pima Indian women diabetes data containing genuine missing values.

Four speakers including myself are invited to present their recent extraordinary works related to innovative statistical modelling for complex data in the 64th ISI World Statistics Congress - Ottawa, Canada (July 16-20, 2023). The information of the four speakers is delivered below.
The 1st presenter:
Professor Victor Hugo Lachos Davila 
Affiliation: Department of Statistics, University of Connecticut, Storrs, USA 
Email: hlachos@uconn.edu
Presenting Title: The Use of the EM Algorithm for Regularization Problems in High-Dimensional Linear Mixed-Effects Models
The 2nd presenter:
Professor Wan-Lun Wang
Affiliation: Department of Statistics and Institute of Data Science, National Cheng Kung University, Taiwan 
Email: wangwl@gs.ncku.edu.tw
Presenting Title: A selection approach to multivariate linear mixed models with censored and non-ignorable missing responses 
The 3rd presenter:
Professor Luis Mauricio Castro
Affiliation: Department of Statistics, Pontificia Universidad Católica de Chile, Chile 
Email: mcastro@mat.uc.cl
Presenting Title: Using joint random partition models for flexible change point analysis in multivariate processes 
The 4th presenter:
Professor Tsung-I Lin
Affiliation: Institute of Statistics, National Chung Hsing University, Taiwan 
Email: tilin@nchu.edu.tw
Presenting Title: Flexible clustering for asymmetric data via mixtures of unrestricted skew normal factor analyzers

Organiser: Prof. Tsung-I Lin 

Chair: Prof. Tsung-I Lin 

Speaker: Victor Hugo Lachos Davila  

Speaker: Wan-Lun Wang 

Speaker: Luis Mauricio Castro