2018 Fall

Date: 2018-11-23 Time: 15:30-16:30 Location: BURN 1104 Abstract: This is not a research talk. Rather, the goal is to address the topic of the talk title through a 2017 multi-authored paper published in Nature Human Behaviour. The Nature article proposes that the standard cut-off significance level of .05 should be replaced by a cut-off level of .005 when new discoveries are being claimed. The authors attribute the high proportion of irreducible results in the literature that accompany claimed new discoveries, in part, to the low-bar cut-off of .

Date: 2018-11-16 Time: 15:30-16:30 Location: BURN 1104 Abstract: The median residual lifetime function is a statistical quantity which describes the future point in time at which the probability of current survival has dropped by 50%. In deriving an estimator for the median residual lifetime function for length-biased data, the added features of left-truncation and right-censoring must be taken into account. In this talk, we give a brief description of length-biased failure time data and show that by using a particular non-parametric estimator for the survival function that it is possible to derive the asymptotically most-efficient non-parametric estimator for the median residual lifetime function.

Date: 2018-11-09 Time: 15:30-16:30 Location: BURN 1104 Abstract: Density estimation lies at the intersection of statistics, theoretical computer science, and machine learning. We review some old and new results on the sample complexities (also known as minimax convergence rates) of estimating densities of high-dimensional distributions, in particular mixtures of Gaussians and Ising models. Based on joint work with Hassan Ashtiani, Shai Ben-David, Luc Devroye, Nick Harvey, Christopher Liaw, Yani Plan, and Tommy Reddad.

Date: 2018-11-02 Time: 15:30-16:30 Location: BURN 1104 Abstract: The rigorous mathematical treatment of random fragmentation-coalescent models in the literature is difficult to find, and perhaps for good reason. We examine two different types of random fragmentation-coalescent models which produce somewhat unexpected results. The first concerns an agent-based model in which, with a rate that depends on the configuration of the system, agents coalesce into clusters that also fragment into their individual constituent membership.

Date: 2018-10-26 Time: 15:30-16:30 Location: BURN 1104 Abstract: In this talk, I will first briefly introduce my research on object oriented data analysis with application to neuroimaging studies. I will then talk about a detailed example on imaging genetics. In this project, we develop a high-dimensional matrix linear regression model to correlate 2D imaging responses with high-dimensional genetic covariates. We propose a fast and efficient screening procedure based on the spectral norm to deal with the case that the dimension of scalar covariates is much larger than the sample size.

Date: 2018-10-19 Time: 15:30-16:30 Location: BURN 1104 Abstract: Optimal transport plays an increasingly relevant and useful role in the theory and application of mixture model based clustering and inference. In this talk I will describe some recent progress in characterizing the convergence behavior of mixing distributions when one fits a mixture model to the data. This theory hinges on the relationship between the space of mixture densities, which is endowed with variational or Hellinger distance, and the space of mixing measures endowed with optimal transport distance metrics.

Date: 2018-10-12 Time: 16:00- Location: CRM, Université de Montréal, Pavillon André-Aisenstadt, salle 6254 Abstract: Many variables encountered in practice (e.g., economic variables) have skewed distributions. The latter provide a conducive ground for the presence of influential observations, which are those that have a drastic impact on the estimates if they were to be excluded from the sample. We examine the problem of influential observations in a classical statistic setting as well as in a finite population setting that includes two main frameworks: the design-based framework and the model-based framework.

Date: 2018-10-05 Time: 15:30-16:30 Location: BURN 1104 Abstract: In this talk, we discuss how sufficient dimension reduction can be used to aid causal inference. We propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Compared with the original covariates and the propensity scores, which are commonly used for matching in the literature, the reduced covariates are estimable nonparametrically and are effective in imputing the missing potential outcomes.

Date: 2018-09-28 Time: 15:30-16:30 Location: BURN 1205 Abstract: Constructing an optimal dynamic treatment regime become complex when there are large number of prognostic factors, such as patient’s genetic information, demographic characteristics, medical history over time. Existing methods only focus on selecting the important variables for the decision-making process and fall short in providing inference for the selected model. We fill this gap by leveraging the conditional selective inference methodology. We show that the proposed method is asymptotically valid given certain rate assumptions in semiparametric regression.

Date: 2018-09-21 Time: 09:30-10:15 Location: Bronfman Building 001 Abstract: We present Possession Sketches, a new machine learning method for organizing and exploring a database of basketball player-tracks. Our method organizes basketball possessions by offensive structure. We first develop a model for populating a dictionary of short, repeated, and spatially registered actions. Each action corresponds to an interpretable type of player movement. We examine statistical patterns in these actions, and show how they can be used to describe individual player behavior.