Advances and Innovations in Statistics and Data Science, 1st ed. 2022 ICSA Book Series in Statistics Series

This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.

1. MiRNA-Gene Activity Interaction Networks (miGAIn): Integrated joint models of miRNA-gene targeting and disturbance in signal processing.- 2. Feature Screening for Ultrahigh-Dimensional Regression with Error-Prone Varables.- 3. Cosine Distribution in the Post-selection Inference of Least Angle Regression.- 4. Learning Finite Gaussian Mixture via Wasserstein Distance.- 5. An Entropy-based Method with Word Embedding Clustering for Comment Ranking.- 6. Estimation in Functional Linear Model with Incomplete Functional Observations.- 7. A Flexible Linear Single Index Proportional Hazards Regression Model for Multivariate Survival Data.- 8. Efficient Estimation of Semiparametric Linear Transformation Model with Left-Truncated and Current Status Data.- 9. Flexible Transformations for Modeling Compositional Data.- 10. Identifiability and Estimation of Autoregressive ARCH Models with Measurement Error.- 11. Modal Regression for Skewed, Truncated, or Contaminated Data with Outliers.- 12. Spatial Multilevel Modeling in the Galveston Bay Recovery Study Survey.- 13. Efficient Experimental Design for Regularized Linear Models.- 14. A Selective Overview of Statistical Models for Identification of Treatment-sensitive Subset.- 15. Analysis of Discrete Compositional Series While Accounting for Informative Time-dependent Cluster Sizes with Application to Air Pollution Related Emergency Room Visits.

Wenqing He is Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario, Canada. He was the Program Chair for the 4^th ICSA-Canada Chpater Symposium.

Liqun Wang is Professor and Department Head in the Department of Statistics at the University of Manitoba, Canada. He is the Past-Chair of the ICSA-Canada Chapter. 

Jiahua Chen is Professor and Canada Research Chair (Tier I) in the Department of Statistics at the University of British Columbia, Canada.

Chunfang Devon Lin is Associate Professor in the Department of Mathematics and Statistics at Queen’s University, Canada. She was the local chair of the for the 4^th ICSA-Canada Chapter Symposium.

Promotes methodology development in data science, statistics, and biostatistics Showcases the latest work as presented at the 4th ICSA-Canada Chapter Symposium Includes applications to a variety of fields, including health, economics, the social sciences, and more