2018 Winter

Date: 2018-04-27 Time: 15:30-16:30 Location: BURN 1205 Abstract: To explore the impact of length-biased sampling on the evaluation of risk factors of nosocomial infections in point-prevalence studies. We used cohort data with full information including the exact date of the nosocomial infection and mimicked an artificial one-day prevalence study by picking a sample from this cohort study. Based on the cohort data, we studied the underlying multi-state model which accounts for nosocomial infection as an intermediate and discharge/death as competing events.

Date: 2018-04-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Standard clustering algorithms can find regular-structured clusters such as ellipsoidally- or spherically-dispersed groups, but are more challenged with groups lacking formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial algorithm that can be used with the computationally efficient k-means or other algorithms.

Date: 2018-04-06 Time: 15:30-16:30 Location: BURN 1205 Abstract: With modern technology development, functional responses are observed frequently in various scientific fields including neuroimaging data analysis. Empirical likelihood as a nonparametric data-driven technique has become an important statistical inference methodology. In this paper, motivated by diffusion tensor imaging (DTI) data we propose three generalized empirical likelihood-based methods that accommodate within-curve dependence on the varying coefficient model with functional responses and embed a robust regression idea.

Date: 2018-03-23 Time: 15:30-16:30 Location: BURN 1205 Abstract: Computer experiments refer to the study of real systems using complex simulation models. They have been widely used as efficient, economical alternatives to physical experiments. Computer experiments with time series outputs are called dynamic computer experiments. In this talk, we consider two problems of such experiments: emulation of large-scale dynamic computer experiments and inverse problem. For the first problem, we proposed a computationally efficient modelling approach which sequentially finds a set of local design points based on a new criterion specifically designed for emulating dynamic computer simulators.

Date: 2018-03-16 Time: 15:30-16:30 Location: BURN 1205 Abstract: Over the last decade, advances in measurement technologies has enabled researchers to generate multiple types of high-dimensional “omics” datasets for large cohorts. These data provide an opportunity to derive a mechanistic understanding of human complex traits. However, inferring meaningful biological relationships from these data is challenging due to high-dimensionality , noise, and abundance of confounding factors. In this talk, I’ll describe statistical approaches for robust analysis of genomic data from large population studies, with a focus on 1) understanding the nature of confounding and approaches for addressing them and 2) understanding the genomic correlates of aging and dementia.

Date: 2018-02-23 Time: 15:30-16:30 Location: BURN 1205 Abstract: Sparse penalized quantile regression is a useful tool for variable selection, robust estimation, and heteroscedasticity detection in high-dimensional data analysis. We discuss the variable selection and estimation properties of the lasso and folded concave penalized quantile regression via non-asymptotic arguments. We also consider consistent parameter tuning therein. The computational issue of the sparse penalized quantile regression has not yet been fully resolved in the literature, due to non-smoothness of the quantile regression loss function.

Date: 2018-02-16 Time: 15:30-16:30 Location: McGill University, OTTO MAASS 217 Abstract: For over a century now, we statisticians have successfully convinced ourselves and almost everyone else, that in statistical inference the size of the population N can be ignored, especially when it is large. Instead, we focused on the size of the sample, n, the key driving force for both the Law of Large Numbers and the Central Limit Theorem. We were thus taught that the statistical error (standard error) goes down with n typically at the rate of 1/√n.

Date: 2018-02-09 Time: 15:30-16:30 Location: BURN 1205 Abstract: Multi-drug-resistant tuberculosis (MDR-TB) is defined as strains of tuberculosis that do not respond to at least the two most used anti-TB drugs. After diagnosis, the intensive treatment phase for MDR-TB involves taking several alternative antibiotics concurrently. The Collaborative Group for Meta-analysis of Individual Patient Data in MDR-TB has assembled a large, fused dataset of over 30 observational studies comparing the effectiveness of 15 antibiotics.

Date: 2018-02-02 Time: 15:30-16:30 Location: BURN 1205 Abstract: A new process, the factorial hidden Markov volatility (FHMV) model, is proposed to model financial returns or realized variances. This process is constructed based on a factorial hidden Markov model structure and corresponds to a parsimoniously parametrized hidden Markov model that includes thousands of volatility states. The transition probability matrix of the underlying Markov chain is structured so that the multiplicity of its second largest eigenvalue can be greater than one.

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).