/categories/mcgill-statistics-seminar/index.xml McGill Statistics Seminar - McGill Statistics Seminars

McGill Statistics Seminar

  • Covariates missing by design

    Michael McIsaac · Sep 19, 2014

    Date: 2014-09-19

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    Incomplete data can arise in many different situations for many different reasons. Sometimes the data may be incomplete for reasons beyond the control of the experimenter. However, it is also possible that this missingness is part of the study design. By using a two-phase sampling approach where only a small sub-sample gives complete information, it is possible to greatly reduce the cost of a study and still obtain precise estimates. This talk will introduce the concepts of incomplete data and two-phase sampling designs and will discuss adaptive two-phase designs which exploit information from an internal pilot study to approximate the optimal sampling scheme for an analysis based on mean score estimating equations.

  • Hydrological applications with the functional data analysis framework

    Fateh Chebana · Sep 12, 2014

    Date: 2014-09-12

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    River flows records are an essential data source for a variety of hydrological applications including the prevention of flood risks and as well as the planning and management of water resources. A hydrograph is a graphical representation of the temporal variation of flow over a period of time (continuously measured, usually over a year). A flood hydrograph is commonly characterized by a number of features, mainly its peak, volume and duration. Classical and recent multivariate approaches considered in hydrological applications treated these features jointly in order to take into account their dependence structure or their relationship. However, all these approaches are based on the analysis of a limited number of characteristics and do not make use of the full information provided by the hydrograph. Even though these approaches provided good results, they present some drawbacks and limitations. The objective of the present talk is to introduce a new framework for hydrological applications where data, such as hydrographs, are employed as continuous curves: functional data. In this context, the whole hydrograph is considered as one infinite-dimensional observation. This context contributes to addressing the problem of lack of data commonly encountered in hydrology. A number of functional data analysis tools and methods are presented and adapted.

  • Some aspects of data analysis under confidentiality protection

    Bimal Sinha · Apr 4, 2014

    Date: 2014-04-04

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    Statisticians working in most federal agencies are often faced with two conflicting objectives: (1) collect and publish useful datasets for designing public policies and building scientific theories, and (2) protect confidentiality of data respondents which is essential to uphold public trust, leading to better response rates and data accuracy. In this talk I will provide a survey of two statistical methods currently used at the U.S. Census Bureau: synthetic data and noise perturbed data.

  • How much does the dependence structure matter?

    Ruodu Wang · Mar 28, 2014

    Date: 2014-03-28

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    In this talk, we will look at some classical problems from an anti-traditional perspective. We will consider two problems regarding a sequence of random variables with a given common marginal distribution. First, we will introduce the notion of extreme negative dependence (END), a new benchmark for negative dependence, which is comparable to comonotonicity and independence. Second, we will study the compatibility of the marginal distribution and the limiting distribution when the dependence structure in the sequence is allowed to vary among all possibilities. The results are somewhat simple, yet surprising. We will provide some interpretation and applications of the theoretical results in financial risk management, with the hope to deliver the following message: with the common marginal distribution known and dependence structure unknown, we know essentially nothing about the asymptotic shape of the sum of random variables.

  • Mixed effects trees and forests for clustered data

    Denis Larocque · Mar 14, 2014

    Date: 2014-03-14

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    In this talk, I will present extensions of tree-based and random forest methods for the case of clustered data. The proposed methods can handle unbalanced clusters, allows observations within clusters to be splitted, and can incorporate random effects and observation-level covariates. The basic tree-building algorithm for a continuous outcome is implemented using standard algorithms within the framework of the EM algorithm. The extension to other types of outcomes (e.g., binary, count) uses the penalized quasi-likelihood (PQL) method for the estimation and the EM algorithm for the computation. Simulation results show that the proposed methods provides substantial improvements over standard trees and forests when the random effects are non negligible. The use of the method will be illustrated with real data sets.

  • On the multivariate analysis of neural spike trains: Skellam process with resetting and its applications

    Reza Ramezan · Feb 21, 2014

    Date: 2014-02-21

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    Nerve cells (a.k.a. neurons) communicate via electrochemical waves (action potentials), which are usually called spikes as they are very localized in time. A sequence of consecutive spikes from one neuron is called a spike train. The exact mechanism of information coding in spike trains is still an open problem; however, one popular approach is to model spikes as realizations of an inhomogeneous Poisson process. In this talk, the limitations of the Poisson model are highlighted , and the Skellam Process with Resetting (SPR) is introduced as an alternative model for the analysis of neural spike trains. SPR is biologically justified, and the parameter estimation algorithm developed for it is computationally efficient. To allow for the modelling of neural ensembles, this process is generalized to the multivariate case, where Multivariate Skellam Process with Resetting (MSPR), as well as the multivariate Skellam distribution are introduced. Simulation and real data studies confirm the promising results of the Skellam model in the statistical analysis of neural spike trains.

  • Divergence based inference for general estimating equations

    Anand N. Vidyashankar · Feb 14, 2014

    Date: 2014-02-14

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    Hellinger distance and its variants have long been used in the theory of robust statistics to develop inferential tools that are more robust than the maximum likelihood but as ecient as the MLE when the posited model holds. A key aspect of this alternative approach requires speci cation of a parametric family, which is usually not feasible in the context of problems involving complex data structures wherein estimating equations are typically used for inference. In this presentation, we describe how to extend the scope of divergence theory for inferential problems involving estimating equations and describe useful algorithms for their computation. Additionally, we theoretically study the robustness properties of the methods and establish the semi-parametric eciency of the new divergence based estimators under suitable technical conditions. Finally, we use the proposed methods to develop robust sure screening methods for ultra high dimensional problems. Theory of large deviations, convexity theory, and concentration inequalities play an essential role in the theoretical analysis and numerical development. Applications from equine parasitology, stochastic optimization, and antimicrobial resistance will be used to describe various aspects of the proposed methods.

  • Statistical techniques for the normalization and segmentation of structural MRI

    Taki Shinohara · Feb 7, 2014

    Date: 2014-02-07

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature has centered on histogram matching and other histogram mapping techniques, but little focus has been on normalizing images to have biologically interpretable units. We explore this key goal for statistical analysis and the impact of normalization on cross-sectional and longitudinal segmentation of pathology.

  • An exchangeable Kendall's tau for clustered data

    Hela Romdhani · Jan 31, 2014

    Date: 2014-01-31

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    I’ll introduce the exchangeable Kendall’s tau as a nonparametric intra class association measure in a clustered data frame and provide an estimator for this measure. The asymptotic properties of this estimator are investigated under a multivariate exchangeable cdf. Two applications of the proposed statistic are considered. The first is an estimator of the intraclass correlation coefficient for data drawn from an elliptical distribution. The second is a semi-parametric intraclass independence test based on the exchangeable Kendall’s tau.

  • An introduction to stochastic partial differential equations and intermittency

    Raluca Balan · Jan 10, 2014

    Date: 2014-01-10

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    In a seminal article in 1944, Itô introduced the stochastic integral with respect to the Brownian motion, which turned out to be one of the most fruitful ideas in mathematics in the 20th century. This lead to the development of stochastic analysis, a field which includes the study of stochastic partial differential equations (SPDEs). One of the approaches for the study of SPDEs was initiated by Walsh (1986) and relies on the concept of random-field solution for equations perturbed by a space-time white noise (or Brownian sheet). This concept allows us to investigate the dynamical changes in the probabilistic behavior of the solution, simultaneously in time and space. These developments will be reviewed in the first part of the talk. The second part of the talk will be dedicated to some recent advances in this area, related to the existence of a random-field solution for some classical SPDEs (like the stochastic heat equation) perturbed by a colored'' noise, which behaves in time like the fractional Brownian motion. When this solution exists, it exhibits a strong form of intermittency,’’ a property which was originally introduced in the physics literature for describing random fields whose values develop very large peaks. This talk is based on some recent joint work with Daniel Conus (Lehigh University).