Spring 2021 Seminars

The Department of Statistics Seminar Series will be conducted remotely via Zoom this semester. All event times are listed in Eastern Standard Time. Speakers for this semester include:

Wednesday, January 20 at 12:30 PM

Mahrad Sharifvaghefi, Assistant Professor of Economics at University of Pittsburgh

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Title: Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks

Abstract: This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or expo- nential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both variable selec- tion and forecasting stages. In this study, we investigate whether or not we should use weighted observations at the variable selection stage in the presence of structural breaks, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches we focus on the recently developed One Covariate at a time Multiple Testing (OCMT) method that allows a natural distinction between the selection and forecasting stages, and provide theoretical justification for using the full (not down-weighted) sample in the selection stage of OCMT and down-weighting of observations only at the forecasting stage (if needed). The benefits of the proposed method are illustrated by empirical applications to forecasting output growths and stock market returns.


Wednesday, January 27 at 12:30 PM

Rina Barber, Professor of Statistics at University of Chicago

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Title: Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling

Abstract: Goodness-of-fit (GoF) testing is ubiquitous in statistics, with direct ties to model selection, confidence interval construction, conditional independence testing, and multiple testing, just to name a few applications. While testing the GoF of a simple (point) null hypothesis provides an analyst great flexibility in the choice of test statistic while still ensuring validity, most GoF tests for composite null hypotheses are far more constrained, as the test statistic must have a tractable distribution over the entire null model space. A notable exception is co-sufficient sampling (CSS): resampling the data conditional on a sufficient statistic for the null model guarantees valid GoF testing using any test statistic the analyst chooses. But CSS testing requires the null model to have a compact (in an information-theoretic sense) sufficient statistic, which only holds for a very limited class of models; even for a null model as simple as logistic regression, CSS testing is powerless. In this paper, we leverage the concept of approximate sufficiency to generalize CSS testing to essentially any parametric model with an asymptotically-efficient estimator; we call our extension “approximate CSS” (aCSS) testing. We quantify the finite-sample Type I error inflation of aCSS testing and show that it is vanishing under standard maximum likelihood asymptotics, for any choice of test statistic. We apply our proposed procedure both theoretically and in simulation to a number of models of interest to demonstrate its finite-sample Type I error and power. This work is joint with Lucas Janson.


Wednesday, February 3 at 12:30 PM

Yi Yu, Associate Professor of Statistics at University of Warwick

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Title: Functional Linear Regression with Mixed Predictors

Abstract: We study a functional linear regression model that deals with functional responses and allows for both functional covariates and high-dimensional vector covariates. The proposed model is flexible and nests several functional regression models in the literature as special cases. Based on the theory of reproducing kernel Hilbert spaces (RKHS), we propose a penalized least squares estimator that can accommodate functional variables observed on discrete grids. Besides the conventional smoothness penalties, a group Lasso-type penalty is further imposed to induce sparsity in the high-dimensional vector predictors. We derive finite sample theoretical guarantees and show that the excess prediction risk of our estimator is minimax optimal. Furthermore, our analysis reveals an interesting phase transition phenomenon that the optimal excess risk is determined jointly by the smoothness and the sparsity of the functional regression coefficients. A novel efficient optimization algorithm based on iterative coordinate descent is devised to handle the smoothness and sparsity penalties simultaneously. Simulation studies and real data applications illustrate the promising performance of the proposed approach compared to the state-of-the-art methods in the literature.


Wednesday, February 10 at 12:30 PM

Zhiguo Li, Associate Professor of Biostatistics & Bioinformatics at Duke University

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Title: A New Robust and Powerful Weighted Logrank Test

Abstract: In the weighted logrank tests such as Fleming-Harrington test and the Tarone-Ware test, certain weights are used to put more weight on early, middle or late events. The purpose is to maximize the power of the test. The optimal weight under an alternative depends on the true hazard functions of the groups being compared, and thus cannot be applied directly. We propose replacing the true hazard functions with their estimates and then using the estimated weights in a weighted logrank test. However, the resulting test does not control type I error correctly because the weights converge to 0 under the null in large samples. We then adjust the estimated optimal weights for correct type I error control while the resulting test still achieves improved power compared to existing weighted logrank tests, and it is shown to be robust in various scenarios. Extensive simulation is carried out to assess the proposed method and it is applied in several clinical studies in lung cancer.


Wednesday, February 17 at 12:30 PM

Anderson (Ye) Zhang, Assistant Professor of Statistics at University of Pennsylvania

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Title: Optimal Ranking Recovery from Pairwise Comparisons

Abstract: Ranking from pairwise comparisons is a central problem in a wide range of learning and social contexts. Researchers in various disciplines have made significant methodological and theoretical contributions to it. However, many fundamental statistical properties remain unclear especially for the recovery of ranking structure. This talk presents two recent projects towards optimal ranking recovery, under the Bradley-Terry-Luce (BTL) model.

In the first project, we study the problem of top-k ranking. That is, to optimally identify the set of top-k players. We derive the minimax rate and show that it can be achieved by MLE. On the other hand, we show another popular algorithm, the spectral method, is in general suboptimal.  It turns out the leading constants of the sample complexity are different for the two algorithms.

In the second project, we study the problem of full ranking among all players. The minimax rate exhibits a transition between an exponential rate and a polynomial rate depending on the magnitude of the signal-to-noise ratio of the problem. To the best of our knowledge, this phenomenon is unique to full ranking and has not been seen in any other statistical estimation problem. A divide-and-conquer ranking algorithm is proposed to achieve the minimax rate.


Wednesday, February 24 at 12:30 PM

Paromita Dubey, Stein Fellow (Lecturer) in Statistics at Stanford University

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Title: Functional Models for Time Varying Random Objects

Abstract: In recent years, samples of time-varying object data such as time-varying networks that are not in a vector space have been increasingly collected. These data can be viewed as elements of a general metric space that lacks local or global linear structure and therefore common approaches that have been used with great success for the analysis of functional data, such as functional principal component analysis, cannot be applied directly.

In this talk, I will propose some recent advances along this direction.  First, I will discuss ways to obtain dominant modes of variations in time varying object data. I will describe metric covariance, a novel association measure for paired object data lying in a metric space (\Omega d) that we use to define a metric auto-covariance function for a sample of random \Omega -valued curves, where \Omega will not have a vector space or manifold structure. The proposed metric auto-covariance function is non-negative definite when the squared metric d^2 is of negative type. Then the eigenfunctions of the linear operator with the auto-covariance function as kernel can be used as building blocks for an object functional principal component analysis for \Omega-valued functional data, including time-varying probability distributions, covariance matrices and time-dynamic networks. Then I will describe how to obtain analogues of functional principal components for time-varying objects by applying Fréchet means and projections of distance functions of the random object trajectories in the directions of the eigenfunctions, leading to real-valued Fréchet scores and object valued Fréchet integrals. This talk is based on joint work with Hans-Georg Müller.


Wednesday, March 3 at 12:30 PM

Sayar Karmakar, Assistant Professor of Statistics at University of Florida

Title: Time-varying models and applications

Abstract: In this talk I will discuss several time-varying models and their applications. A major motivation of such models emanate from the field of econometrics but these are also very prevalent in several other areas such as medical sciences, climatology etc. Whenever a time-series dataset is observed over a large period of time, it is natural to assume the coefficient parameters also vary over time.

We start with some of the inferential results and analysis using the time-varying analogue of the popular ARCH GARCH model in a frequentist set-up. A criticism of kernel-based estimation lies in the fact that they need huge sample size for reasonable coverage. However, it is important to note that very little has been done so far in the corresponding Bayesian regime with non-gaussian dependent data. One of the key reasons for this lack was the challenge to establish a suitable posterior contraction rate when the independence is taken away.

Here, in the bayesian front, I will talk about two recent works where we deal with Poisson ARX type models and ARCH GARCH type time-varying models. Our estimations are B-spline based and we establish optimal contraction of the posterior computed via Hamiltonian Monte Carlo. We conclude the talk by discussing two applications: a. the Covid-19 spread in NYC through the tvPoisson model and b. Predictive performance comparison of the Bayesian and frequentist tv(G)ARCHmodel applied on some real datasets.

Check back for announcements of additional seminar speakers. A Zoom link for each event will be sent via email to Department of Statistics faculty and students. Other interested Pitt community members should email srh75@pitt.edu to request the link.