CRM-SSC Prize Address

Date: 2022-09-30 Time: 15:30-16:30 (Montreal time) HTTPS://US06WEB.ZOOM.US/J/84226701306?PWD=UEZ5NVPZAULLDW5QNU8VZZIVBEJXQT09 MEETING ID: 842 2670 1306 PASSCODE: 692788 Abstract: Capture-recapture experiments are widely used to collect data needed to estimate the abundance of a closed population. To account for heterogeneity in the capture probabilities, Huggins (1989) and Alho (1990) proposed a semiparametric model in which the capture probabilities are modelled parametrically and the distribution of individual characteristics is left unspecified. A conditional likelihood method was then proposed to obtain point estimates and Wald-type confidence intervals for the abundance.

Date: 2019-10-04 Time: 16:00-17:00 Location: CRM, UdeM, Pav. André-Aisenstadt, 2920, ch. de la Tour, salle 1355 Abstract: Modeling dependence between random variables is omnipresent in statistics. When rare events with high impact are involved, such as severe storms, floods or heat waves, the issue is both of great importance for risk management and theoretically challenging. Combining extreme-value theory with copula modeling and rank-based inference yields a particularly flexible and promising approach to this problem.

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: 2018-01-26 Time: 15:30-16:30 Location: ROOM 6254 Pavillon Andre-Aisenstadt 2920, UdeM Abstract: Linear regression remains an important framework in the era of big and complex data. In this talk I present some recent examples where we resort to the classical simple linear regression model and its celebrated extensions in novel settings. The Eureka moment came while reading Wu and Guan’s (2015) comments on our generalized Kruskal-Wallis (GKW) test (Elif Acar and Sun 2013, Biometrics).

Date: 2017-01-27 Time: 15:30-16:30 Location: ROOM 6254 Pavillon Andre-Aisenstadt 2920, UdeM Abstract: Conditional copula models describe dynamic changes in dependence and are useful in establishing high dimensional dependence structures or in joint modelling of response vectors in regression settings. We describe some of the methods developed for estimating the calibration function when multiple predictors are needed and for resolving some of the model choice questions concerning the selection of copula families and the shape of the calibration function.

Date: 2016-02-11 Time: 16:00-17:00 Location: CRM 6254 (U. de Montréal) Abstract: Principal components analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate observations. In the functional case, a new characterization of elliptical distributions on separable Hilbert spaces allows us to obtain an equivalent stochastic optimality property for the principal component subspaces of random elements on separable Hilbert spaces. This property holds even when second moments do not exist.

Date: 2015-01-15 Time: 16:00-17:00 Location: CRM 1360 (U. de Montréal) Abstract: Functional data analysis (FDA) has received substantial attention, with applications arising from various disciplines, such as engineering, public health, finance etc. In general, the FDA approaches focus on nonparametric underlying models that assume the data are observed from realizations of stochastic processes satisfying some regularity conditions, e.g., smoothness constraints. The estimation and inference procedures usually do not depend on merely a finite number of parameters, which contrasts with parametric models, and exploit techniques, such as smoothing methods and dimension reduction, that allow data to speak for themselves.

Date: 2013-02-22 Time: 14:30-15:30 Location: CRM, Université de Montréal, Pav. André-Ainsenstadt, salle 1360 Abstract: In this talk, we first provide an overview of issues arising from and methods dealing with complex survey data in the presence of missing observations, with a major focus on the estimating equation approach for analysis and imputation methods for missing data. We then propose a semiparametric fractional imputation method for handling item nonresponses, assuming certain baseline auxiliary variables can be observed for all units in the sample.