2023 Fall

Date: 2023-11-17 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/81865630475 Meeting ID: 818 6563 0475 Passcode: None Abstract: We consider the high-dimensional linear regression model and assume that a fraction of the responses are contaminated by an adversary with complete knowledge of the data and the underlying distribution. We are interested in the situation when the dense additive noise can be heavy-tailed but the predictors have sub-Gaussian distribution.

Date: 2023-11-10 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/89337793218 Meeting ID: 893 3779 3218 Passcode: None Abstract: This paper aims to use copulas to derive estimators of the health concentration curve and Gini coefficient for health distribution. We highlight the importance of expressing health inequality measures in terms of a copula, which we in turn use to build copula-based semi and nonparametric estimators of the above measures. Thereafter, we study the asymptotic properties of these estimators.

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.