CANSSI Ontario STatistics Seminar (CAST): Cindy Feng

Join us at the CANSSI Ontario STatistics Seminars (CAST) with

Dr. Cindy Feng
Associate Professor
Department of Community Health and Epidemiology
Faculty of Medicine
Dalhousie University 

Free Online Event | Registration Required

Residual Diagnostics for Count and Censored Regression Models

Abstract
Diagnosing regression models is essential for evaluating model fit and detecting discrepancies between the model and data. While traditional residuals like Pearson and deviance residuals are effective for linear models, they face limitations when applied to count and censored regression models. Count data often produce residuals that deviate significantly from normality, and censored data introduces additional complexities that traditional methods struggle to address. In this presentation, we explore the challenges and innovations in residual diagnostics for count and censored regression models. We evaluate randomized quantile residuals (RQRs) and their performance in diagnosing count regression models, including zero-inflated models. Despite their established use, RQRs have not been extensively studied in this context, and our research addresses this gap through simulation studies.

Additionally, we introduce a novel approach for censored regression models using normalized randomized survival probabilities (RSPs). This method transforms the survival probabilities of censored observations into uniformly distributed random numbers, which are then converted into normally distributed residuals. Our study demonstrates that these normalized RSP residuals provide a robust diagnostic tool for censored regression models, effectively detecting model misspecification that traditional residuals might miss. Our findings highlight the effectiveness of these novel residual diagnostics in addressing the unique challenges posed by count and censored regression models. These advancements offer valuable tools for improving model assessment and diagnostic accuracy in statistical analysis.

Speaker Profile
Dr. Feng is an Associate Professor in Biostatistics in the Department of Community Health and Epidemiology, Faculty of Medicine at Dalhousie University. She obtained her MSc and PhD in Statistics from Simon Fraser University. Dr. Feng’s research focuses on developing biostatistical models for analyzing correlated data, including repeated measurements, hierarchical clustering, multiple outcome types, and spatially correlated data. Her research has been funded by NSERC, Research Nova Scotia, the Canadian Statistical Sciences Institute, and MITACS, etc. Dr. Feng is dedicated to bridging the gap between statistical methods and practice, pursuing both methodological development and the application of statistical methods in public health. This commitment has led her to establish partnerships with researchers across various disciplines, including medicine, psychology, environmental science, and sociology. In addition to her research, Dr. Feng is actively involved in teaching and mentoring, having supervised graduate students and contributed to curriculum development in biostatistics. She has also served on professional committees at the Statistical Society of Canada.