Past Seminar Series

Date: 2024-04-12 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/86957985232 Meeting ID: 869 5798 5232 Passcode: None Abstract: One of the fascinating phenomena of spin glasses is the dramatic change in behavior that occurs between the high and low temperature regimes. In addition to its physical meaning, this phase transition corresponds to a detection threshold with respect to the signal-to-noise ratio in a spiked matrix model. The free energy of the spherical Sherrington-Kirkpatrick (SSK) model has Gaussian fluctuations at high temperature, but Tracy-Widom fluctuations at low temperature.

Date: 2024-03-22 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/81895414756 Meeting ID: 818 9541 4756 Passcode: None Abstract: This paper introduces several enhancements to the minimum covariance determinant method of outlier detection and robust estimation of means and covariances. We leverage the principal component transform to achieve dimension reduction and ultimately better analyses. Our best subset selection algorithm strategically combines statistical depth and concentration steps. To ascertain the appropriate subset size and number of principal components, we introduce a bootstrap procedure that estimates the instability of the best subset algorithm.

Date: 2024-03-01 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/89811237909 Meeting ID: 898 1123 7909 Passcode: None Abstract: Electronic health records (EHR) data contain rich information about patients’ health condition, comorbidities, clinical outcomes, and drug prescriptions. They are often used to draw causal inferences and compare different treatments’ effectiveness. However, these data are not experimental. They present with special features that should be addressed or that may affect the inference.

Date: 2024-02-16 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/82678428848 Meeting ID: 826 7842 8848 Passcode: None Abstract: DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, missing values is an issue and appropriate imputation techniques are important to avoid an unnecessary sample size reduction as well as to optimally leverage the information collected.

Date: 2024-02-09 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/87465663442 Meeting ID: 874 6566 3442 Passcode: None Abstract: Networks arise naturally in many scientific fields as a representation of pairwise connections. Statistical network analysis has most often considered a single large network, but it is common in a number of applications, for example, neuroimaging, to observe multiple networks on a shared node set. When these networks are grouped by case-control status or another categorical covariate, the classical statistical question of two-sample comparison arises.

Date: 2024-02-02 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/89669635642 Meeting ID: 896 6963 5642 Passcode: None Abstract: In this work, we investigate the asymptotic properties of Le Cam’s one-step estimator for weak Fractionally AutoRegressive Integrated Moving-Average (FARIMA) models. For these models, noises are uncorrelated but neither necessarily independent nor martingale differences errors. We show under some regularity assumptions that the one-step estimator is strongly consistent and asymptotically normal with the same asymptotic variance as the least squares estimator.

Date: 2024-01-19 Time: 15:30-16:30 (Montreal time) Location: Online, retransmitted in Burnside 1104 https://mcgill.zoom.us/j/85422946487 Meeting ID: 854 2294 6487 Passcode: None Abstract: Estimation of biomarkers related to disease classification and modeling of its progression is essential for treatment development for Alzheimer’s Disease (AD). The task is more daunting for characterizing relatively rare AD subtypes such as the early-onset AD. In this talk, I will describe the Longitudinal Alzheimer’s Disease Study (LEADS) intending to collect and publicly distribute clinical, imaging, genetic, and other types of data from people with EOAD, as well as cognitively normal (CN) controls and people with early-onset non-amyloid positive (EOnonAD) dementias.

Date: 2024-01-12 Time: 15:30-16:30 (Montreal time) Location: In person, Burnside 1104 https://mcgill.zoom.us/j/83008174313 Meeting ID: 830 0817 4313 Passcode: None Abstract: Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this talk, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies.

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.