Husky Union Building (HUB)
Husky Union Building (HUB)
Multiplicative Models for Register Data
Registers are increasingly important sources of data to be analyzed. Examples include registers of congenital abnormalities, supermarket purchases, or traffic violations. In such registers, records are created when a relevant event is observed, and they contain the features characterizing the event. Understanding the structure of associations among the features is of primary interest. However, the registers often do not contain cases in which no feature is present and therefore, standard multiplicative or loglinear models may not be applicable.
Essential Regression
We introduce the Essential Regression model, which provides an alternative to the ubiquitous Ksparse high dimensional linear regression on p variables. While Ksparse regression assumes that only K components of the observable X directly influence Y , Essential Regression allows for all components of X to influence Y , but mediated through a Kdimensional random vector Z.
Instrumental Variable Learning of Marginal Structural Models
In a seminal paper, Robins (1998) introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of timevarying treatment regimes in complex longitudinal studies subject to timevarying confounding. He established identification of MSM parameters under a sequential randomization assumption (SRA), which rules out unmeasured confounding of treatment assignment over time.
[CANCELLED] Statistical Methods for Two Problems in Biology
Note 2/7/2018: We are canceling this seminar as a precaution in anticipation of the expected Winter storm.
As the pace and scale of data collection continues to increase across all areas of biology, there is a growing need for effective and principled statistical methods for the analysis of the resulting data. In this talk, I'll describe two ongoing projects to help fill this gap.
A New Standard for the Analysis and Design of Replication Studies
A new standard is proposed for the evidential assessment of replication studies. The approach combines a specific reverseBayes technique with priorpredictive tail probabilities to define replication success. The method gives rise to a quantitative measure for replication success, called the sceptical pvalue. The sceptical pvalue integrates traditional significance of both the original and replication study with a comparison of the respective effect sizes.
How Statistics Took Me to the Aleutian Islands
Did you know that your skills in statistics can be applied to ensure natural resources, such as fish, wildlife and even ecosystems, remain resilient into the future? That your love of algebra can take you to wild, remote, and amazing places? That there are careers where you get to collaborate with a wide variety of dedicated scientists working to better understand the world, how it is changing, and what it will be like in the future?
Bayesian Approaches to Dynamic Model Selection
In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or ``normal" behavior. In this talk, I will first discuss a principled Bayesian approach for estimating time varying functional connectivity networks from brain fMRI data. Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature.
Spectral Gap in Random Bipartite Biregular Graphs and Applications
The asymptotics of the secondlargest eigenvalue in random regular graphs (also referred to as the "Alon conjecture") have been computed by Joel Friedman in his celebrated 2004 paper. Recently, a new proof of this result has been given by Charles Bordenave, using the nonbacktracking operator and the IharaBass formula. In the same spirit, we have been able to translate Bordenave's ideas to bipartite biregular graphs in order to calculate the asymptotical value of the secondlargest pair of eigenvalues, and obtained a similar spectral gap result.
Fast Inference for Spatial Generalized Linear Mixed Models
NonGaussian spatial data arise in a number of disciplines. Examples include spatial data on disease incidences (counts), and satellite images of ice sheets (presenceabsence). Spatial generalized linear mixed models (SGLMMs), which build on latent Gaussian processes or Markov random fields, are convenient and flexible models for such data and are used widely in mainstream statistics and other disciplines. For highdimensional data, SGLMMs present significant computational challenges due to the large number of dependent spatial random effects.
Student Poster Session
Interested in what our graduate students have been working on? Come join us for posters and presentations by the students themselves as they present their research.
Volunteer presenters include:
Interactive algorithms for multiple hypothesis testing
Data science is at a crossroads. Each year, thousands of new data scientists are entering science and technology, after a broad training in a variety of fields. Modern data science is often exploratory in nature, with datasets being collected and dissected in an interactive manner. Classical guarantees that accompany many statistical methods are often invalidated by their nonstandard interactive use, resulting in an underestimated risk of falsely discovering correlations or patterns.
Locally stationary spatiotemporal interpolation of Argo profiling float data
Argo floats measure sea water temperature and salinity in the upper 2,000 m of the global ocean. The statistical analysis of the resulting spatiotemporal data set is challenging due to its nonstationary structure and large size. I propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatiotemporal prediction are carried out in a movingwindow fashion. This yields computationally tractable nonstationary anomaly fields without the need to explicitly model the nonstationary covariance structure.
Estimation and testing for twostage experiments in the presence of interference
Many important causal questions concern interactions between units, also known as interference. Examples include interactions between individuals in households, students in schools, and firms in markets. Standard analyses that ignore interference can often break down in this setting: estimators can be badly biased, while classical randomization tests can be invalid. In this talk, I present recent results on estimation and testing for twostage experiments, which are powerful designs for assessing interference.
Pacific Northwest Statistics Meeting 1996
Paul Gustafson, Department of Statistics, University of British Columbia Hierarchical Bayesian Modelling for Survival Data Hierarchical Bayes models can be flexible tools for the analysis of failure time data. This will be illustrated by two examples. The first example is in a clinical trials context, when there are several response times for each patient, and many patients at each clinical centre. Frailties are used to model both acrosspatient variability and acrosscentre variability.
Your Dreams May Come True with MTP2
We study maximum likelihood estimation for exponential families that are multivariate totally positive of order two (MTP2). Such distributions appear in the context of ferromagnetism in the Ising model and various latent models, as for example Brownian motion tree models used in phylogenetics. We show that maximum likelihood estimation for MTP2 exponential families is a convex optimization problem. For quadratic exponential families such as Ising models and Gaussian graphical models, we show that MTP2 implies sparsity of the underlying graph without the need of a tuning parameter.
Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity
Abstract: 

Rotational post hoc transformations have traditionally played a key role in enhancing the interpretability of factor analysis. Regularization methods also serve to achieve this goal by prioritizing sparse loading matrices. In this work, we bridge these two paradigms with a unifying Bayesian framework. Our approach deploys intermediate factor rotations throughout the learning process, greatly enhancing the effectiveness of sparsity inducing priors. Statistical Inference for Infectious Disease Modeling
