When: Thursday, August 27, 2026 — 2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speaker: Dr. Nikos Ignatiadis, Department of Statistics, University of Chicago

Abstract: A common task in high-throughput biology is to screen for associations across thousands of units of interest, e.g., genes or proteins. Often, the data for each unit are modeled as Gaussian measurements with unknown mean and variance and are summarized as per-unit sample averages and sample variances. The downstream goal is multiple testing for the means. In this domain, it is routine to "moderate" (that is, to shrink) the sample variances through parametric empirical Bayes methods before computing p-values for the means. Such an approach is asymmetric in that a prior is posited and estimated for the nuisance parameters (variances) but not the primary parameters (means). Our work initiates the formal study of this paradigm, which we term "empirical partially Bayes multiple testing." In this framework, if the prior for the variances were known, one could proceed by computing p-values conditional on the sample variances---a strategy called partially Bayes inference by Sir David Cox. We show that these conditional p-values satisfy an Eddington/Tweedie-type formula and are approximated at nearly-parametric rates when the prior is estimated by nonparametric maximum likelihood. The estimated p-values can be used with the Benjamini-Hochberg procedure to guarantee asymptotic control of the false discovery rate. Even in the compound setting, wherein the variances are fixed, the approach retains asymptotic type-I error guarantees.

When: Thursday, September 3, 2026 — 2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speaker: Dr. Rebecca Killick, Department of Mathematics and Statistical Sciences, Clemson University

Abstract: Multiple changepoint analyses have become an important tool in modern statistics. Classical approaches to the problem include dynamic programming, binary segmentation procedures and their variants, and windowed approaches. Fast dynamic programming procedures that optimize penalized likelihoods only apply when all model parameters change at each and every changepoint time, which is often physically unrealistic. Similarly, windowed approaches either assume that all dynamics change at each changepoint time, or that aspects that do not change at the changepoint time vary across windows. Penalized likelihood methods for the general case, where only a subset of parameters are allowed to change at the changepoint times, require extensive computational searches, classically done via genetic algorithms, to locate the optimal changepoint configuration. This talk discusses a new method that rapidly estimate optimal penalized likelihood changepoint configurations in the general case, bypassing the slow computational (and stochastic) drawbacks of genetic algorithms. Consistency of the changepoint configuration and model parameters under infill asymptotics are proven; the procedure is shown to work well in finite samples via simulation. Applications to environmental and business problems are detailed.