Statistical Applications

Chair : Chun-houh Chen, Director, ISSAS

09:00Hiroe Tsubaki
Construction of an information system for analyzing micro data collected in Japanese government statistical surveys and its application to EBPM.

09:25Stephen Wu
Online Link
Data-driven design of liquid-crystalline polyimides by interplay with expert knowledge.

09:50Rahul Roy
On a model of evolution of Subspecies.

10:15Frederick Kin Hing Phoa
Biomedical Oriented Logistic Dantzig Selector (BOLD Selector) for biomarker selection and its applications to patient group differentiation across Parkinsonism spectrum.

Poster Lightning Session

Chair : Yen-Tsung Huang, ISSAS

01 Ya-Shan Cheng

02 Wei-Yang Yu

03 Yi-Ting Huang

04 Po-Chun Chen

05 En-Yu Lai

06 Yi-Ju Lee

07 Jih-Chang Yu

08 Jia-Ying Su

09 Yuan-Jung Juang

10 Sheng-Zhan Hua

11 Hsin-Ping Liu

Biomedical Studies and Precision Medicine

Chair : Hsuan-Yu Chen, ISSAS

13:10Hsin-Chou Yang
The convergence of artificial intelligence and biobank in precision medicine.

13:35Masayuki Henmi
A confidence interval robust to publication bias for random-effects meta-analysis of few studies.

14:00Naoko Kato-Nitta
Information and risk avoidance behavior During Covid-19 pandemic.

14:20Meghna Bose
Prior effective sample size in Phase-II clinical trials with mixed binary and continuous responses.

14:40Chen-Hsiang Yeang
Cooperative stability renders protein complex formation more robust and controllable.

Causal Inference and Bayesian Methods

Chair : Yi-Hau Chen, ISSAS

15:25Jayant Jha
Fully Bayesian estimation of virtual brain parameters.

15:50Kiranmoy Das
Bayesian quantile joint modelling with an application to leukemia maintenance studies.

16:15Sayantan Paul
Consistent group selection using global-local prior in high dimensional setup.

16:35Yoshiyuki Ninomiya
Information criteria for sparse methods in causal inference.

17:00Yen-Tsung Huang
Causal inference in the multiverse of hazard.

Statistical Computing and Machine Learning

Chair : Tso-Jung Yen, ISSAS

09:00Kengo Kamatani
Non-reversible Guided Metropolis algorithm.

09:25Chien-Ming Chi
FACT: High-Dimensional Random Forests Inference.

09:50Toshimitsu Aritake
Unsupervised domain adaptation for extra features using optimal transport.

10:10Hironori Fujisawa
Transfer learning via 𝐿₁ -regularization.

10:35Su-Yun Huang
Robust aggregation for federated learning.

Guided Tour

Bus leaves HSS Building at 13:30

Network Analysis

Chair : Ting-Li Chen, ISSAS

09:00Antar Bandyopadhyay
De-preferential attachment model through preferential attachment.

09:25Arindam Chatterjee
Prediction of the average degree of a network using network sampling: a model assisted approach.

09:50Soham Sarkar
Covariance networks for functional data on multidimensional domains.

10:15Hideitsu Hino
Information geometry of covariate shift algorithms.

Multivariate Data and Time Series Analysis

Chair : Junho Yang, ISSAS

11:00 Keisuke Yano
Minimum information dependence model for mixed-domain data and its application.

11:25Yoshinori Kawasaki
Comparative VaR backtesting: GARCH-EVT versus GARCH-UGH.

11:50Hendri Sutrisno
Pseudo periodic time series segmentation: An optimization approach.

Statistical Models and Tests I

Chair : Hsin-wen Chang, ISSAS

13:10Ming-Yueh Huang
Improved semiparametric estimation of the proportional rate model with recurrent event data.

13:35Jia-Han Shih
Regression association: from concordance to predictability.

13:55Anik Roy
A novel image monitoring procedure using jump regression.

14:15Kei Noba
On stochastic control under Poisson observations: optimality of a barrier strategy in a general Lévy model.

14:40Junho Yang
Feature estimation and testing for high-dimensional linear regression with dependent regressors.

Statistical Models and Tests II

Chair : Frederick Kin Hing Phoa, ISSAS

15:25Kotaro Sakamoto
Automatic termination of a score-based generative modelling.

15:45Bilol Banerjee
Testing distributional equality for functional random variables.

16:05Shyamal Krishna De
Exact distribution-free k-sample tests for high dimension, low sample size data.

16:30Ming-Chung Chang
Supervised Stratified Subsampling for Big Data Reduction.