McGill Statistics Seminar

Date: 2023-11-03 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/2571023554 Meeting ID: 257 102 3554 Passcode: None Abstract: Classical vector autoregressive (VAR) models have long been a popular choice for modeling multivariate time series data due to their flexibility and ease of use. However, the VAR model suffers from overparameterization which is a serious issue for high-dimensional time series data as it restricts the number of variables and lags that can be incorporated into the model.

Date: 2023-10-27 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/84195498572 Meeting ID: 841 9549 8572 Passcode: None Abstract: In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event, conventional methods adjusting for left truncation tend to rely on the (quasi-)independence assumption that the truncation time and the event time are “independent" on the observed region.

Date: 2023-10-20 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/89761165882 Meeting ID: 897 6116 5882 Passcode: None Abstract: Functional data is defined as any random variables that assume values in an infinite precision domain, such as time or space. In applications, this data is usually discretely observed at some regularly or irregularly-spaced points over the domain. In this talk, we discuss ways to adapt modern neural network architectures for the analysis of functional data.

Date: 2023-10-13 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/83477865796 Meeting ID: 834 7786 5796 Passcode: None Abstract: Distance plays a pivotal role in statistics. Meanwhile, recent technologies and social networks have yielded large complex network data sets, which require customized statistical tools. From a mathematical viewpoint, these complex networks are graphs with non-trivial structures (in contrast to Erdös-Rényi graphs, for example). These networks are models of systemic phenomena and cases where individual-level analyses are insufficient.

Date: 2023-09-29 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/82440807026 Meeting ID: 824 4080 7026 Passcode: None Abstract: Doubly robust estimation under the marginal structural Cox model has been a challenge until recently due to the non-collapsibility of the Cox regression model. This is because the estimand of causal hazard ratio assumes that the marginal structural Cox model holds, while the doubly robust estimating function requires the specification of an additional model for the conditional distribution of the time-to-event given treatment and covariates, both models unlikely to hold simultaneously.

Date: 2023-09-22 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/89374813252 Meeting ID: 893 7481 3252 Passcode: None Abstract: Influential diagnosis is an integral part of data analysis, of which most existing methodological frameworks presume a deterministic submodel and are designed for low-dimensional data (i.e., the number of predictors $p$ smaller than the sample size $n$). However, the stochastic selection of a submodel from high-dimensional data where $p$ exceeds $n$ has become ubiquitous.

Date: 2023-09-15 Time: 15:30-16:30 (Montreal time) Location: Burnside 1104 https://mcgill.zoom.us/j/86404798712 Meeting ID: 864 0479 8712 Passcode: None Abstract: Causal mediation techniques are a means for identifying the degree to which a cause influences its effect along particular causal paths. For example, in a model where a cause influences its effect both indirectly via a mediator and directly via factors not included in the model, mediation techniques enable one to measure both direct and indirect effects.

Date: 2023-08-17 Time: 15:30-16:30 (Montreal time) Hybrid: In person / Zoom Location: Burnside Hall 1104 https://mcgill.zoom.us/j/89623344755?pwd=S1E0QWVjSm8wRHdIYU5IZzllSXNjUT09 Meeting ID: 896 2334 4755 Passcode: 287381 Abstract: In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR). We develop an empirical Bayes approach to controlling the FDX. We show that for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to FDX constraint.

Date: 2023-08-14 Time: 15:30-16:30 (Montreal time) Hybrid: In person / Zoom Location: Burnside Hall 1104 https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: We study a multivariate response regression model where each coordinate is described by a location-scale regression, and where the dependence structure of the “noise” terms in the regression is described by a parametric copula. Our goal is to estimate the associated Euclidean copula parameter given a sample of the response and the covariate.

Date: 2023-03-24 Time: 15:30-16:30 (Montreal time) On Zoom only https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09 Meeting ID: 834 3668 6293 Passcode: 12345 Abstract: Causal discovery procedures are popular methods for discovering causal structure across the physical, biological, and social sciences. However, most procedures for causal discovery only output a single estimated causal model or single equivalence class of models. We propose a procedure for quantifying uncertainty in causal discovery. Specifically, we consider linear structural equation models with non-Gaussian errors and propose a procedure which returns a confidence sets of causal orderings which are not ruled out by the data.