McGill Statistics Seminar

Date: 2019-10-25

Time: 15:30-16:30

Location: BURN 1205

Abstract:

High-dimensional point processes have become ubiquitous in many scientific fields. For instance, neuroscientists use calcium florescent imaging to monitor the firing of thousands of neurons in live animals. In this talk, I will discuss new methodological, computational and theoretical developments for learning neuronal connectivity networks from high-dimensional point processes. Time permitting, I will also discuss a new approach for handling non-stationarity in high-dimensional time series.

Date: 2019-10-18

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In its most common form extreme value theory is concerned with the limiting distribution of location-scale transformed block-maxima $M_n = \max(X_1,\dots,X_n)$ of a sequence of identically distributed random variables $(X_i)$, $i\geq 1$. In case the members of the sequence $(X_i)$ are independent, the weak limiting behaviour of $M_n$ is adequately described by the classical Fisher-Tippett-Gnedenko theorem. In this presentation we are interested in the case of dependent random variables $(X_i)$ while retaining a common marginal distribution function $F$ for all $X_i$, $i\in\mathbb{N}$. Complementary to the well established extreme value theory in a time series setting we consider a framework in which the dependence between (extreme) events does not decay over time. This approach is facilitated by highlighting the connection between block-maxima and copula diagonals in an asymptotic context. The main goal of this presentation is to discuss a generalization of the Fisher–Tippett–Gnedenko theorem in this setting, leading to limiting distributions that are not in the class of generalized extreme value distributions. This result is exemplified for popular dependence structures related to extreme value, Archimedean and Archimax copulas. Focusing on the class of hierarchical Archimedean copulas the results can further be extended to the multivariate setting. Finally, we illustrate the resulting limit laws and discuss their properties.

Date: 2019-10-11

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Integral estimation in any dimension is an extensive topic, largely treated in the literature, with a broad range of applications. Monte-Carlo type methods arise naturally when one looks forward to quantifying/controlling the error. Many methods have already been developped: MCMC, Poisson disk sampling, QMC (and randomized versions), Bayesian quadrature, etc. In this talk, I’ll consider a different approach which consists in defining the quadrature nodes as the realization of a spatial point process. In particular I’ll show that a very specific class of determinantal point processes, a class of repulsive point patterns, has excellent properties and is able to estimate efficiently integrals for non-differentiable functions with an explicit and faster rate of convergence than current methods.

Date: 2019-09-27

Time: 15:30-16:30

Location: McIntyre Medical Building, Room 521

Abstract:

This work is motivated by a problem in describing forest nitrogen cycling, and a consequent goal of constructing regression models for spatial images. Specifically, I present a functional concurrent linear model (FLCM) with varying coefficients for two-dimensional spatial images. To address overparameterization issues, the parameter surfaces in this model are transformed into the wavelet domain and then sparse representations are found using two different methods: LASSO and Bayesian variable selection. I will briefly discuss extensions to address missing data problems for colocated spatial images and the modeling of tree species in landscape ecology. In addition I will discuss the use of the sextant in marine navigation.

Date: 2019-09-20

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Symmetry has played a significant role in modern physics, in part by constraining the physical laws. I will discuss how it could play a fundamental role in AI by constraining the deep model design. In particular, I focus on discrete domain symmetries and through examples show how we can use this inductive bias as a principled means for constraining a feedforward layer and significantly improving its sample efficiency.

Date: 2019-09-13

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In the current era of multi-omics and digital healthcare, we are facing unprecedented amount of data with tremendous opportunities to link molecular phenotypes with complex diseases. However, the lack of integrative statistical method hinders system-level interrogation of relevant disease-related pathways and the genetic implication in various healthcare outcome.

In this talk, I will present our current progress in mining genomics and healthcare data. In particular, I will cover two main topics: (1) a statistical approach to assess gene set enrichments using genetic and transcriptomic data; (2) multimodal latent topic model for mining electronic healthcare and whole genome sequencing data from small patient cohort.

Date: 2019-09-06

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In this talk, we discuss two problems of semiparametric estimation and variable selection for length-biased data with heavy censoring. The common feature of the proposed estimation procedures in the literature is that they only put probability mass on failure times. Under length-biased sampling, however, censoring is informative and failing to incorporate censored observations into estimation can lead to a substantial loss of efficiency. We propose two estimation procedures by computing the likelihood contribution of both uncensored and censored observations. For variable selection problem, we introduce a unified penalized estimating function and use an optimization algorithm to solve it. We discuss the asymptotic properties of the resulting penalized estimators. The work is motivated by the International stroke Trial dataset collected in Argentina in which the survival times of about 88% of the 545 cases are censored.

Date: 2019-04-26

Time: 15:30-16:30

Location: BURNSIDE 1205

Abstract:

Graphs, a general type of data structures for capturing interconnected objects, are ubiquitous in a variety of disciplines and domains ranging from computational social science, recommender systems, medicine, bioinformatics to chemistry. Representative examples of real-world graphs include social networks, user-item networks, protein-protein interaction networks, and molecular structures, which are represented as graphs. In this talk, I will introduce our work on learning effective representations of graphs such as learning low-dimensional node representations of large graphs (e.g., social networks, protein-protein interaction graphs, and knowledge graphs) and learning representations of entire graphs (e.g., molecule structures).

Date: 2019-04-12

Time: 15:30-16:30

Location: BURNSIDE 1205

Abstract:

Advances in wearables and digital technology now make it possible to deliver behavioral, mobile health, interventions to individuals in their every-day life. The micro-randomized trial (MRT) is increasingly used to provide data to inform the construction of these interventions. This work is motivated by multiple MRTs that have been conducted or are currently in the field in which the primary outcome is a longitudinal binary outcome. The first, often called the primary, analysis in these trials is a marginal analysis that seeks to answer whether the data indicates that a particular intervention component has an effect on the longitudinal binary outcome. Under rather restrictive assumptions one can, based on existing literature, derive a semi-parametric, locally efficient estimator of the causal effect. In this talk, starting from this estimator, we develop multiple estimators that can be used as the basis of a primary analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the MRT, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.

Date: 2019-04-05

Time: 15:30-16:30

Location: BURNSIDE 1104

Abstract:

Estimating individual treatment effects is crucial for individualized or precision medicine. In reality, however, there is no way to obtain both the treated and untreated outcomes from the same person at the same time. An approximation can be obtained from randomized controlled trials (RCTs). Despite the limitations that randomizations are usually expensive, impractical or unethical, pre-specified variables may still not fully incorporate all the relevant characteristics capturing individual heterogeneity in treatment response. In this work, we use non-experimental data; we model heterogenous treatment effects in the studied population and provide a Bayesian estimator of the individual treatment response. More specifically, we develop a novel Bayesian nonparametric (BNP) method that leverages the G-computation formula to adjust for time-varying confounding in observational data, and it flexibly models sequential data to provide posterior inference over the treatment response at both group level and individual level. On a challenging dataset containing time series from patients admitted to intensive care unit (ICU), our approach reveals that these patients have heterogenous responses to the treatments used in managing kidney function. We also show that on held out data the resulting predicted outcome in response to treatment (or no treatment) is more accurate than alternative approaches.