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Abstract: Hidden Markov models (HMMs) can infer biological rhythms by linking observations to underlying latent states of the biological process, often assuming state transition probabilities follow fixed, regular cycles. However, rhythms can fluctuate due to internal and external factors. I extend HMMs to model “irregular rhythms” that vary in frequency or stability over time. I analyze motor activity data from patients with depression to infer their circadian rhythms, which repeat every 24 hours but often exhibit irregularities. To jointly model state transition probabilities across all patients, accounting for daily behavioural cycles, daily variability, and individual variability, I formulate these probabilities to depend on time-of-day, day, and random effects. This approach provides insights into the regularities and trends of state-switching dynamics, revealing that transition probabilities do not always adhere to a regular daily cycle. Overall, this work advances the modelling of irregular rhythms in HMMs and contributes to a deeper understanding of circadian-related health issues.

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: The Gibbs sampler is a Markov Chain Monte Carlo algorithm that iteratively samples from the conditional distributions of a probability measure of interest and is widely used in computational statistics. Under the assumption of log-concavity, for its random scan version, we provide a sharp bound on the speed of convergence in relative entropy. Assuming that evaluating conditionals is cheap compared to evaluating the joint density, our results imply that the number of full evaluations required for the Gibbs sampler to mix grows linearly with the condition number and is independent of the dimension. This contrasts with gradient-based methods, whose mixing time typically increases with the dimension. Our techniques also allow us to analyze Metropolis-within-Gibbs schemes, as well as the Hit-and-Run algorithm. This is joint work with Filippo Ascolani and Giacomo Zanella. 

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: The work of the Flexible Learning in Statistics Group ranges from conducting studies of important aspects of statistics education to developing and testing resources for difficult statistics concepts. In this seminar, students will present several recent projects: using student focus groups to assess Shiny apps, developing and testing interactive resources to improve understanding of Bayesian inference, enhancing Stat 251 labs by creating active learning material and introducing pre-lab quizzes, and conducting a study of the impact of exam question wording on the performance of students with English as an Additional Language (EAL). You’ll also hear about StatEngage, the ASDa-led project to guide students through the challenges of consulting.

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: Many interventions in causal inference can be represented as transformations of the variables of interest. Abstracting interventions in this way allows us to identify a local symmetry property exhibited by many causal models under interventions. Where present, this symmetry can be characterized by a type of map called a cocycle, an object that is central to dynamical systems theory. We show that such cocycles exist under general conditions and are sufficient to identify interventional distributions and, under suitable assumptions, counterfactual distributions. We use these results to derive cocycle-based estimators for causal estimands and show that they achieve semiparametric efficiency under standard conditions. Since entire families of distributions can share the same cocycle, these estimators can make causal inference robust to mis-specification by sidestepping superfluous modelling assumptions. We demonstrate both robustness and state-of-the-art performance in several simulations, and apply our method to estimate the effects of 401(k) pension plan eligibility on asset accumulation using a real dataset.

Joint work with Hugh Dance (UCL/Gatsby Unit): https://arxiv.org/abs/2405.13844

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: The presence of big data, characterized by exceptionally large sample size, often brings the challenge of outliers and data distributions that exhibit heavy tails. An online learning estimation that incorporates anti-outlier capabilities while not relying on historical data is therefore urgently required to achieve robust and efficient estimators. In this talk, we introduce an innovative online learning approach based on a mode kernel-based objective function, specifically designed to address outliers and heavy-tailed distributions in the context of big data. The developed approach leverages mode regression within an online learning framework that operates on data subsets, which enables the continuous updating of historical data using pertinent information extracted from a new data subset. We demonstrate that the resulting estimator is asymptotically equivalent to the mode estimator calculated using the entire dataset. Monte Carlo simulations and an empirical study are presented to illustrate the finite sample performance of the proposed estimator.

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: This session will be a perspective of what working as a statistics consultant in a contract research organisation for pharmaceutical/biotech companies entails. In addition to an overview of potential career paths, the specific critical tasks and responsibilities involved for a statistician working in real-world data will be discussed.

A look into the type of statistical methodologies through case studies will be provided, demonstrating how they play a role in drug development, regulatory submissions, and health technology assessments. This sets the stage for the discussion of potential research collaborations between UBC students and industry, where students can have the opportunity to advance health research whilst gaining experience on whether a career in health research is of interest.

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract:  Hidden Markov models (HMMs) are commonly used to identify latent processes from observed time series, but it is challenging to fit them to large and complex time series collected by modern sensors. Using data from threatened resident killer whales (Orcinus orca) off the western coast of Canada as a case study, we provide solutions to three common challenges faced when identifying latent behaviour from complicated biologging data. First, biologging time series often violate common assumptions of HMMs when collected at high frequencies. We thus propose a hierarchical approach which utilizes moving-window Fourier analysis to capture fine-scale dependence structures. Second, modern technology allows researchers to directly label the latent process of interest, but rare labels can have a negligible influence on parameter estimates. We introduce a weighted likelihood approach that increases the relative influence of labelled observations. Third, applying HMMs to large time series is computationally demanding, so we propose a novel EM algorithm that combines a partial E step with variance-reduced stochastic optimization within the M step. These solutions allow researchers to model biologging data with HMMs that are more interpretable, accurate, and efficient to fit than existing methods.

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract: We discuss issues arising from methodological developments related to inverse probability weighting and model-based prediction with non-probability survey samples, with focuses on the validity of estimation procedures for participation probabilities (propensity scores) and the impact of key assumptions on inferential approaches. We also explore strategies for dealing with undercoverage problems due to violations of the positivity assumption.   

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Presentation 1

Time: 11:00am – 11:30am

Speaker: Charlotte Edgar, UBC Statistics MSc student

Title: Cellwise Robust Covariance-Regularized Regression for High-Dimensional Data

Abstract: It is common to use regularization methods when dealing with high-dimensional regression problems. The scout family, developed by Witten and Tibshirani in 2009, is a class of covariance-regularized regression procedures suitable for prediction in high-dimensional settings. The scout procedure estimates the inverse covariance matrix through two log-likelihood maximization steps that each allow for regularization and then uses the estimated inverse covariance matrix to obtain estimates of the regression coefficients. The aim of this project was to make the scout procedure robust to cellwise outliers. Cellwise outliers are common in high-dimensional datasets and recent work has led to cellwise robust covariance estimates that could be used in the scout procedure. We assess the predictive performance of robust plug-in estimators and outlier detection methods. The development of a regression method that is robust to cellwise outliers, encourages sparsity, and can be applied in high-dimensional settings would be valuable to many fields, such as genomics, and is an area undergoing current research.

Presentation 2

Time: 11:30am – 12:00pm

Speaker: Graeme Kempf, UBC Statistics MSc student

Title: The impact of disease-modifying drugs for multiple sclerosis on hospitalizations and mortality in British Columbia: A retrospective study using an illness-death multi-state model

Abstract: The efficacy of disease-modifying drugs (DMDs) for multiple sclerosis was established in clinical trials that were short and excluded older individuals and individuals living with comorbidities. This has led to a lack of knowledge of the effects of chronic DMD use and the effects of DMDs on individuals that do not meet the traditional eligibility criteria for clinical trials. Multi-state models are a technique which can advance the understanding of a disease beyond that offered by time-to-event models alone. The long-term, real-world efficacy of DMDs was explored by applying a multi-state model to administrative healthcare data. Whether exposure to any DMD is associated with fewer hospitalizations, shorter hospitalizations, and/or a reduction in the chance of dying inside or outside the hospital was investigated using multi-state techniques such as intensity-based analysis and pseudo-value regression.