Winter 2017 Seminar Series

Wednesday, January 25, 2017

Generating Marketing Insights from Social Media Data

Time: 11:00 a.m.

Speaker: Jennifer Cutler, Assistant Professor of Marketing, Kellogg School of Management, Northwestern University

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract: Social media contain a wealth of information about consumers and market structures, and this has many marketers excited about the potential uses for such data. However, it is often not straightforward or cost-effective to process these data for reliable marketing insights. In particular, many approaches to commonly desired classification and prediction tasks (such as classifying text by topic or classifying users by demographics) rely on supervised learning methods, which require (often extensive) labelled training data. Such data can be difficult to obtain, and, due to the idiosyncratic and rapidly evolving user behavior on different platforms (e.g., “netspeak” slang), can become out-of-date quickly. In this talk, I will explore ways of leveraging the organic structure of social media data to circumvent the need for curated training data, resulting in unsupervised or distantly-supervised algorithms that are flexible, scalable, and highly automated. I will share examples of how such methods can be applied towards problems such as classifying marketer and user-generated text by topic, predicting demographic traits of users, and estimating the strength of brand image associations.

Wednesday, February 8, 2017

Inference in High-dimensional Semi-parametric Graphical Models

Time: 11:00 a.m.

Speaker: Mladen Kolar, Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract: In this talk, we discuss root-n consistent estimators for elements of the latent precision matrix under high-dimensional elliptical copula models. Under mild conditions, the estimator is shown to be asymptotically normal, which allows for construction of tests about presence of edges in the underlying graphical model. The asymptotic distribution is robust to model selection mistakes and does not require non-zero elements to be separated away from zero. The key technical result is a new lemma on the “sign-subgaussian” property, which allows us to establish optimality of the estimator under the same conditions as in the gaussian setting. Extension to dynamic elliptical copula models will also be presented.

Wednesday, March 1, 2017

Graphical Models via Joint Quantile Regression with Component Selection

Time: 11:00 a.m.

Speaker: Hyonho Chun, Assistant Professor, Department of Statistics, Purdue University

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract: A graphical model is used for describing interrelationships among multiple variables. In many cases, the multivariate Gaussian assumption is made partly for its simplicity but the assumption is hardly met in actual applications. In order to avoid dependence on a rather strong assumption, we propose to infer the graphical model via joint quantile regression with component selection, since the components of quantile regression carry information to infer the conditional independence.  We demonstrate the advantages of our approach using simulation studies and apply our method to an interesting real biological dataset, where the dependence structure is highly complex.

Wednesday, March 8, 2017

Time: 11:00 a.m.

Speaker: Giorgio Primicer, Professor, Department of Economics, Northwestern University

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road