2015 Winter

Date: 2015-05-14 Time: 15:30-16:30 Location: BURN 1205 Abstract: A bivariate distribution can sometimes be characterized completely by properties of its conditional distributions. In this talk, we will discuss models of bivariate distributions whose conditionals are members of prescribed parametric families of distributions. Some relevant models with specified conditionals will be discussed, including the normal and lognormal cases, the skew-normal and other families of distributions. Finally, some conditionally specified densities will be shown to provide convenient flexible conjugate prior families in certain multiparameter Bayesian settings.

Date: 2015-05-07 Time: 15:30-16:30 Location: Université de Sherbrooke Abstract: This talk looks at some recent climate controversies from a statistical standpoint. The issues are motivated via changepoints and their detection. Changepoints are ubiquitous features in climatic time series, occurring whenever stations relocate or gauges are changed. Ignoring changepoints can produce spurious trend conclusions. Changepoint tests involving cumulative sums, likelihood ratio, and maximums of F-statistics are introduced; the asymptotic distributions of these statistics are quantified under the changepoint-free null hypothesis.

Date: 2015-03-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Networks provide a useful means to summarize sparse yet structured massive datasets, and so are an important aspect of the theory of big data. A key question in this setting is to test for the significance of community structure or what in social networks is termed homophily, the tendency of nodes to be connected based on similar characteristics. Network models where a single parameter per node governs the propensity of connection are popular in practice, because they are simple to understand and analyze.

Date: 2015-03-13 Time: 15:30-16:30 Location: BURN 1205 Abstract: Despite almost universal acceptance across most fields of statistics, Bayesian inferential methods have yet to breakthrough to widespread use in causal inference, despite Bayesian arguments being a core component of early developments in the field. Some quasi-Bayesian procedures have been proposed, but often these approaches rely on heuristic, sometimes flawed, arguments. In this talk I will discuss some formulations of classical causal inference problems from the perspective of standard Bayesian representations, and propose some inferential solutions.

Date: 2015-02-27 Time: 15:30-16:30 Location: BURN 1205 Abstract: I will talk about COMPASS, a new Bayesian hierarchical framework for characterizing functional differences in antigen-specific T cells by leveraging high-throughput, single-cell flow cytometry data. In particular, I will illustrate, using a variety of data sets, how COMPASS can reveal subtle and complex changes in antigen-specific T-cell activation profiles that correlate with biological endpoints. Applying COMPASS to data from the RV144 (“the Thai trial”) HIV clinical trial, it identified novel T-cell subsets that were inverse correlates of HIV infection risk.

Date: 2015-02-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Rapidly progressing particle tracking techniques have revealed that foreign particles in biological fluids exhibit rich and at times unexpected behavior, with important consequences for disease diagnosis and drug delivery. Yet, there remains a frustrating lack of coherence in the description of these particles’ motion. Largely this is due to a reliance on functional statistics (e.g., mean-squared displacement) to perform model selection and assess goodness-of-fit.

Date: 2015-02-13 Time: 15:30-16:30 Location: BURN 1205 Abstract: High-dimensional statistics is the basis for analyzing large and complex data sets that are generated by cutting-edge technologies in genetics, neuroscience, astronomy, and many other fields. However, Lasso, Ridge Regression, Graphical Lasso, and other standard methods in high-dimensional statistics depend on tuning parameters that are difficult to calibrate in practice. In this talk, I present two novel approaches to overcome this difficulty. My first approach is based on a novel testing scheme that is inspired by Lepski’s idea for bandwidth selection in non-parametric statistics.

Date: 2015-02-05 Time: 15:30-16:30 Location: BURN 1B39 Abstract: We consider a class of group-lasso learning problems where the objective function is the sum of an empirical loss and the group-lasso penalty. For a class of loss function satisfying a quadratic majorization condition, we derive a unified algorithm called groupwise-majorization-descent (GMD) for efficiently computing the solution paths of the corresponding group-lasso penalized learning problem. GMD allows for general design matrices, without requiring the predictors to be group-wise orthonormal.

Date: 2015-02-02 Time: 15:30-16:30 Location: BURN 1214 Abstract: A copula-based model is described which enables joint analysis of multiple progressive multi-state processes. Unlike intensity-based or frailty-based approaches to joint modeling, the copula formulation proposed herein ensures that a wide range of marginal multi-state processes can be specified and the joint model will retain these marginal features. The copula formulation also facilitates a variety of approaches to estimation and inference including composite likelihood and two-stage estimation procedures.

Date: 2015-01-30 Time: 15:30-16:30 Location: BURN 1205 Abstract: We address two outstanding challenges in sparse regression: (i) computationally efficient estimation in distributed settings; (ii) valid inference for the selected coefficients. The main computational challenge in a distributed setting is harnessing the computational capabilities of all the machines while keeping communication costs low. We devise an approach that requires only a single round of communication among the machines. We show the approach recovers the convergence rate of the (centralized) lasso as long as each machine has access to an adequate number of samples.