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Interactive algorithms for multiple hypothesis testing

Time
Speaker
Aaditya Ramdas

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 non-standard interactive use, resulting in an underestimated risk of falsely discovering correlations or patterns.

Building
Room
145

Locally stationary spatio-temporal interpolation of Argo profiling float data

Time
Speaker
Mikael Kuusela

Argo floats measure sea water temperature and salinity in the upper 2,000 m of the global ocean. The statistical analysis of the resulting spatio-temporal 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 spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the need to explicitly model the nonstationary covariance structure.

Building
Room
332

Estimation and testing for two-stage experiments in the presence of interference

Time
Speaker
Guillaume Basse

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 two-stage experiments, which are powerful designs for assessing interference.

Building
Room
250

Statistical Inference for Infectious Disease Modeling

Time
Speaker
Po-Ling Loh

Abstract:

We discuss two recent results concerning disease modeling on networks. The infection is assumed to spread via contagion (e.g., transmission over the edges of an underlying network). In the first scenario, we observe the infection status of individuals at a particular time instance and the goal is to identify a confidence set of nodes that contain the source of the infection with high probability.

Building
Room
250

Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity

Time
Speaker
Veronika Rockova

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.

Building
Room
337

Your Dreams May Come True with MTP2

Time
Speaker
Caroline Uhler

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.

Building
Room
250

Pacific Northwest Statistics Meeting 1996

Time
Speaker
Paul Gustafson
Richard Olshen

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 across-patient variability and across-centre variability.

Building
Room
Aud