Latest Seminars

Short-term Housing Rentals and Corporatization of Platform Pricing
Dr. Mehmet Gumus, Desautels Faculty of Management, McGill University

In recent years, we have seen the emergence of a number of platforms that facilitate short-term peer-to-peer rentals of assets as part of the bigger collaborative consumption or shared services movement. Although all platforms are similar in that their primary goal is to facilitate asset sharing and the above two channels, they differ in other aspects. In this paper, we focus on one such aspect - how they decide on the price to charge their customers. Specifically, in a platform like AirBnB, the price is effectively set based on a market mechanism that matches supply and demand. But, some other platforms like corporatestays.com and guestbnb.ca are more active. They take turnkey control of the assets and determine the price on "behalf" of the owners that maximizes their profits based on them being paid a share of the price. Our primary goal in this paper is to understand the implications of this difference in pricing strategy for the direct stakeholders of the platform such as customers, owners and the platform as well as indirect stakeholders such as long-term rentals and hotels.

Date 25.02.2019
Time 11:00 am - 12:15 pm
Venue Room 3005, LSK Business Building

Joint Statistics Seminar - High-dimensional Q-learning for Dynamic Treatment Regimes
Prof. Rui Song, Department of Statistics, NC State University

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment ef-fects, especially when the dimension of the tailoring variables is high. In this talk, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard threshold-ing is introduced in the method to eliminate the effects of the nonrespondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by ad-justing the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfac-tory performance for obtaining the proper inference for the value function for the optimal dynamic treat-ment regimes.

Date 19.02.2019
Time 3:00pm – 4:00pm
Venue Room 6045 (LSK Business Building)

It Takes Two to Tango: The Effects of Internal and External Information Integration on Healthcare Process and Outcomes
Prof Hillol Bala, Kelley School of Business, Indiana University

Date 18.02.2019
Time 2:00 - 3:30 pm
Venue ISOM Conference Room 4047, LSK Business Building

Membership-Based Free Shipping Programs: A New Vehicle to Gain Competitive Advantage for Online Retailers?
Mr Geng Sun, Naveen Jindal School of Management, University of Texas at Dallas

Date 15.02.2019
Time 3:15 - 4:45 pm
Venue Room 3003, LSK Business Building

Mechanism Design in Large Cloud Computing Systems
Mr Yingda Zhai, University of Texas at Austin

Date 13.02.2019
Time 3:15 - 4:45 pm
Venue Room 3003, LSK Business Building

Joint Statistics Seminar - Robust Measures of Microstructure Noise
Dr. Merrick Lee, Faculty of Economics, University of Cambridge

We introduce a new nonparametric method to measure microstructure noise, the deviation of the observed asset prices from the fundamental values caused by market imperfections. Using high-frequency data, we provide joint estimators of arbitrary finite moments of microstructure noise, which could be serially dependent and nonstationary. We characterize the limit distribu-tions of the proposed estimators and construct robust confidence intervals under infill asymptot-ics. We further demonstrate a consistency property of our new estimators without any specifica-tion on the data frequencies. As an economic application, we propose two liquidity measures that gauge the instantaneous and average bid-ask spread with potentially autocorrelated order flows, and such measures can be interpreted as an intermediary’s inventory risks to meet liquidi-ty demand. Statistical applications include several model-free tests for the intraday patterns and the zero autocorrelations hypotheses of microstructure noise.

Date 13.02.2019
Time 11:00am – 12:00noon
Venue Room 4047 (LSK Business Building)

On Using the Lottery in Crowdfunding Platforms: Implications for Fundraising and Backer Behavior
Mr Zuyin (Alvin) Zheng, Fox Business School, Temple University

Date 29.01.2019
Time 10:30 am - 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

Joint OM/IE Statistics Seminar - Sequential Nonparametric Tests for a Change in Distribution: an Application to Detecting Radiological Anomalies
Dr. Oscar Hernan Madrid Padilla, Department of Statistics, University of California

In this talk I will propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov--Smirnov statistics.  The approach is simple, robust, highly computationally efficient, easy to calibrate, and requires no parametric assumptions about the underlying null and alternative distributions.  I show that both the false-alarm rate and the power of our procedure are amenable to rigorous analysis, and that the method outperforms existing sequential testing procedures in practice.  I then apply the method to the problem of detecting radiological anomalies, using data collected from measurements of the background gamma-radiation spectrum on a large university campus.  In this context, the proposed method leads to substantial improvements in time-to-detection for the kind of radiological anomalies of interest in law-enforcement and border-security applications.  I will also briefly mention some of my other research directions.

Date 29.01.2019
Time 11:00am – 12:00noon
Venue Room 1003 (LSK Business Building)

Joint Statistics Seminar - Multiple Isotonic Regression
Prof. Cun-Hui Zhang, Rutgers University

We consider multiple isotonic regression with deterministic lattice designs or random designs in a hyper-cube. The least squares estimator (LSE) is known to nearly attain the minimax rate when the range of the unknown mean function is bounded by a positive constant and in the two-dimensional case to nearly achieve the parametric rate when the unknown mean function is piecewise constant in a rectangular parti-tion of the design space. However, there is still a gap between the risk bound for the LSE and the mini-max rate, and the feasibility of adaptation to the parametric rate in the piecewise constant case is unclear in general dimension. As the high entropy of the level set for the LSE could be the culprit behind the an-alytical or possibly real sub-optimality of the LSE, we consider a simpler block estimator instead. We prove that the block estimator attains the minimax rate when the range of the unknown mean is bounded by a constant and achieves the parametric rate up to a logarithmic factor in the piecewise constant case in general dimension. Moreover, perhaps more interestingly, we prove that the block estimator nearly achieves variable selection consistency in the following sense. When the unknown mean function de-pends only on an unknown subset of variables, the block estimator nearly matches the minimax rate based on the oracular knowledge of the set of active variables.

** This is joint work with Hang Deng.

Date 17.01.2019
Time 2:00pm – 3:00pm
Venue Room 4047 (LSK Business Building)

How do Community Members Respond to Commercial Opportunities? Evidence from an Online User Innovation Community
Mr Ohchan Kwon, Harvard Business School

Date 11.01.2019
Time 10:30 am - 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

Generalized Single and Dual Sourcing Models in Inventory Management
Mr Zhe (Jeffrey) Liu, Graduate School of Business, Columbia University

We study a single-item, periodic-review inventory system with one or two supply sources and the simultaneous presence of the following major practical complications: (1) bilateral inventory adjustment options, via procurement orders and salvage sales or returns to the supplier; (2) fixed costs associated with procurement orders and salvage sales or returns; (3) capacity limits associated with upward or downward inventory adjustments. In the single sourcing setting, we characterize the optimal adjustment strategy, both for finite and infinite horizon models, by showing that in each period the inventory position line is to be partitioned into (maximally) five regions. In the dual sourcing setting, the regular and expedited suppliers are differentiated by their per-unit cost and lead time, resulting in a trade-off between cost and responsiveness. We obtain a two-stage optimal policy structure under a unit lead time difference. We develop effective heuristics for general lead time combinations. Numerical studies provide insights into the factors impacting on the value of dual sourcing, that of a salvage option and capacity investments in the primary and dual source.

Date 08.01.2019
Time 11:00 am - 12:15 pm
Venue Room 3003, LSK Business Building

Joint Statistics Seminar - Binary Classification under Different Risk Paradigms
Dr. Lucy Xia, Department of Statistics, Stanford University

Binary classification has attracted intense research effort because of its wide applications in social, biological, and medical studies.  A classifier based on training data will assign a new observation into one of the two classes, and the general goal is to find the best classifier that minimizes a specific risk.  In the traditional paradigm, people study the expected classification error as the risk.

In reality, different applications might have distinct features that require risks tailored to their needs.  It is of great scientific interest to explore other risk paradigms and how they are related to the traditional expected classification errors.  In this talk, I will describe two risk paradigms: the Rayleigh Quotient and Neyman-Pearson paradigms.  Taking into consideration the properties of modern datasets, including heterogeneity, heavy-tailed distributions and high-dimensionality, we construct classifiers under the above two risk paradigms.  The theoretical basis of both classifiers are provided in terms of finite sample oracle inequalities; the performance will be shown via extensive simulation and real data analysis.

Date 08.01.2019
Time 11:00am – 12:00noon
Venue Room 4047 (LSK Business Building)

Does Identity Disclosure Help or Hurt User Content Generation? Social Presence, Inhibition, and Displacement Effects
Mr Jingchuan Pu, Warrington College of Business, University of Florida

Date 04.01.2019
Time 10:30 am ‐ 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

Democratizing Venture Capital Financing for Innovation: Crowdfunding Under Intellectual Property Rights Governance
Mr Zhitao Yin, J. Mack Robinson College of Business, Georgia State University

Date 21.12.2018
Time 10:30 am ‐ 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

Optimal Duration of Innovation Contests
Dr Gizem Korpeoglu, School of Management, University College London

This paper studies the optimal duration and the optimal award scheme of an innovation contest where an organizer elicits solutions to an innovation‐related problem from a group of agents.  Each agent can improve  her  solution  by  exerting  costly effort  but  the  quality  of  her solution  also  depends  on  an  output uncertainty.  We  show,  consistent  with  recent empirical  evidence,  that  the  optimal  contest  duration  and  the optimal total award are positively correlated.  This is because both the optimal contest duration and the optimal total award increase with the agent's output uncertainty and decrease with the marginal impact of the agent's effort on the quality of her solution.  A managerial insight from this result is that the optimal contest duration may increase with the novelty and the sophistication of solutions that the organizer seeks.  More interestingly, we  show  that  it  is  optimal  for  the  organizer  to  give multiple  awards  when  the  organizer  has  low  urgency  in obtaining solutions.  This result may explain why many contests on platforms give multiple awards.

Date 12.12.2018
Time 11:00 am ‐ 12:15 pm
Venue Room 1003, LSK Business Building