# Statistics and Data Science Seminars

**Upcoming Statistics and Data Science Seminars**

**DMS Statistics and Data Science Seminar**

Oct 04, 2023 02:00 PM

ZOOM/354 Parker Hall

Speaker: **Takumi Saegusa**, University of Maryland

**Past Statistics and Data Science Seminars**

**DMS Statistics and Data Science Seminar**

Sep 20, 2023 02:00 PM

354 Parker Hall / ZOOM

**Davide Guzzetti**, Department of Aerospace Engineering, Auburn.

**DMS Statistics and Data Science Seminar**

Sep 13, 2023 02:00 PM

354 Parker Hall

Speaker: Dr. **Yang Chen** (University of Michigan)

**DMS Statistics and Data Science Seminar**

Sep 06, 2023 02:00 PM

354 Parker Hall

Speaker: **Wenying Li**, Auburn University, Assistant Professor of Agriculture Economics

Title: Dimension Reduction: Addressing Aggregation Bias in Large Consumer Demand Systems

Abstract: Building on an insight of Lewbel (1996) that aggregation bias is a special case of the omitted variable bias, we propose two strategies for reducing bias in inconsistently aggregated consumer demand systems. The first uses a penalized lasso approach and the second relies on a residual-based instrumental variable technique to control for the correlation between group prices and the residual in an aggregate demand. In an example, the preferred strategy reduces bias by up to 91% in own-price elasticities and 57% in cross-price elasticities. These strategies are useful to situations where an inconsistently aggregated demand has to be used for practical purposes.

**DMS Statistics and Data Science Seminar**

May 03, 2023 01:00 PM

ZOOM

Speaker: **Yuan Ke** (University of Georgia)

**DMS Statistics and Data Science Seminar**

Apr 26, 2023 01:00 PM

ZOOM

Speaker: **Valérie Chavez **(University of Lausanne)

Title: Extreme value theory and climate extremes

Abstract: The past few decades have seen extreme climate events affecting all regions of the world with catastrophic impacts on human society. Quantifying the risk of such events is difficult but necessary. In this context, methodologies based on extreme value theory play an important role. Extreme value theory (EVT) is the field of statistics dedicated to the study of events with low occurrence frequencies and large amplitudes. In this talk, I will review some basics of EVT and provide some insights on how to use EVT to quantify the risk of such events.

**Short Bio: **Dr. **Valérie Chavez-Demoulin** is a Full Professor of Statistics at HEC Lausanne, University of Lausanne, specializing in statistical methods for quantitative risk management and statistical methodologies applied to operations management in general, and the statistical modeling of extreme events in particular. More recent methodological work concerns conditional dependence structures modeling, non-parametric Bayesian models, dynamic Extreme Value Theory models, and extremes for non-stationary time series. Chavez-Demoulin holds a Ph.D. in Statistics from EPFL. She is an elected member of ISI (The International Statistical Institute).

**DMS Statistics and Data Science Seminar**

Apr 19, 2023 01:00 PM

358 Parker Hall

Speaker: **Xin Bing** (University of Toronto)

Title: Optimal Discriminant Analysis in High-Dimensional Latent Factor Models

Abstract: In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower-dimensional space, and base the classification on the resulting lower-dimensional projections. In this paper, we formulate a latent-variable model with a hidden low-dimensional structure to justify this two-step procedure and to guide which projection to choose. We propose a computationally efficient classifier that takes certain principal components (PCs) of the observed features as projections, with the number of retained PCs selected in a data-driven way. A general theory is established for analyzing such two-step classifiers based on any projections. We derive explicit rates of convergence of the excess risk of the proposed PC-based classifier. The obtained rates are further shown to be optimal up to logarithmic factors in the minimax sense. Our theory allows the lower dimension to grow with the sample size and is also valid even when the feature dimension (greatly) exceeds the sample size. Extensive simulations corroborate our theoretical findings. The proposed method also performs favorably relative to other existing discriminant methods on three real data examples.

**Short Bio. **Dr. Bing holds a Ph.D. degree in statistics from Cornell University. His research interest generally lies in developing new methodologies with theoretical guarantees to tackle modern statistical problems such as high-dimensional statistics, low-rank matrix estimation, multivariate analysis, model-based clustering, latent factor model, topic models, minimax estimation, high-dimensional inference, and statistical and computational trade-offs. He is also interested in the applications of statistical methods to genetics, neuroscience, immunology, and other areas.

**DMS Statistics and Data Science Seminar canceled**

Apr 12, 2023 01:00 PM

ZOOM

**CANCELED**

**DMS Statistics and Data Science Seminar**

Apr 05, 2023 01:00 PM

358 Parker Hall

Speaker: **Carsten Chong **(Columbia University)

Title: Statistical inference for rough volatility: Central limit theorems

Abstract: In recent years, there has been substantive empirical evidence that stochastic volatility is rough. In other words, the local behavior of stochastic volatility is much more irregular than semimartingales and resembles that of a fractional Brownian motion with Hurst parameter H<0.5. In this paper, we derive a consistent and asymptotically mixed normal estimator of H based on high-frequency price observations. In contrast to previous works, we work in a semiparametric setting and do not assume any a priori relationship between volatility estimators and true volatility. Furthermore, our estimator attains a rate of convergence that is known to be optimal in a minimax sense in parametric rough volatility models.

**Short Bio:** Dr. Chong is currently an assistant professor at Columbia University and will join HKUST this summer. Before this, Dr. Chong did a Ph.D. at the Technical University of Munich. His research interests are primarily focused on statistical inference problems for stochastic processes, with an emphasis on high-frequency techniques and applications to financial econometrics. He is also interested in the area of stochastic partial differential equations, in particular, in stochastic PDEs driven by Levy noises, which, in contrast to Gaussian noises, typically have discontinuous and/or heavy-tailed components.

**DMS Statistics and Data Science Seminar**

Mar 29, 2023 01:00 PM

358 Parker Hall

Speaker: **Linxi Liu** (University of Pittsburgh)

**DMS Statistics and Data Science Seminar**

Mar 22, 2023 01:00 PM

ZOOM

Speaker: **Yaqing Chen** (Rutgers University)

Title: Geometric Exploration of Random Objects Through Optimal Transport

Abstract: We propose new tools for the geometric exploration of data objects taking values in a general separable metric space. For a random object, we first introduce the concept of depth profiles. Specifically, the depth profile of a point in a metric space is the distribution of distances between the very point and the random object. Depth profiles can be harnessed to define transport ranks based on optimal transport, which capture the centrality and outlyingness of each element in the metric space with respect to the probability measure induced by the random object. We study the properties of transport ranks and show that they provide an effective device for detecting and visualizing patterns in samples of random objects. In particular, we establish the theoretical guarantees for the estimation of the depth profiles and the transport ranks for a wide class of metric spaces, followed by practical illustrations.

Bio Facts:

**Dr. Chen**, Assistant Professor at Rutgers University, holds a Ph.D. degree in statistics from the University of California, Davis, under the supervision of Dr. Hans-Georg Mueller. Her research interests are mainly in functional data analysis and non-Euclidean data analysis.