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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

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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.

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

Abstract: Computationally expensive integration problems are ubiquitous across statistics and machine learning. This creates a need for methods that approximate integrals well with as few samples as possible. Bayesian quadrature is a probabilistic integration method in which a Gaussian process prior is placed on the integrand, allowing information about properties of the integrand – such as smoothness –  to be used for improved sample efficiency. I will discuss two projects where we used Bayesian quadrature to create better estimators: (1) an improved estimator for maximum mean discrepancy when the measure is a pushforward, and (2) estimators for conditional expectation. In addition, I'll discuss how the choice of prior kernel affects the quality of uncertainty quantification in Gaussian process interpolation (and consequently, Bayesian quadrature), and present a comparison of maximum likelihood and cross-validation estimators that shows that cross-validation is more robust to smoothness misspecification.

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Abstract: Simulated Tempering (ST) is an MCMC algorithm for complex target distributions that operates on a path between the target and an amenable reference distribution. Crucially, if the reference enables i.i.d. sampling, ST is regenerative and therefore embarrassingly parallel. However, the difficulty of tuning ST has hindered its widespread adoption. In this work, we develop a simple nonreversible ST (NRST) algorithm, a general theoretical analysis of ST, and an automated tuning procedure for ST. This procedure enables straightforward integration of NRST into existing probabilistic programming languages. We provide extensive experimental evidence that our tuning scheme improves the performance and robustness of NRST algorithms on a diverse set of probabilistic models.

NRST can be seen as a meta-MCMC algorithm, in that an explorer Markov chain is required to make local moves within distributions in the path, while NRST orchestrates movement along the path. Gradient-based methods like Metropolis-adjusted Langevin algorithm (MALA) produce Markov chains that scale favorably with dimension. However, MALA depends critically on a step size parameter, and tuning it requires too much work to be useful for NRST. To resolve this issue we introduce autoMALA, an improved version of MALA that automatically sets its step size at each iteration based on the local geometry of the target distribution. We prove that autoMALA preserves the target measure despite continual adjustments of the step size. Our experiments demonstrate that autoMALA is competitive with related state-of-the-art MCMC methods, in terms of the number of density evaluations per effective sample, and it outperforms state-of-the-art samplers on targets with varying geometries.