Key Management and Key Pre-distribution
Speaker: Dr. Bimal Roy, Director, Indian Statistical Institute, Kolkata, India
In modern Cryptography, the security of a cryptosystem lies on secrecy of the key, not on secrecy of the encryption algorithm. Hence key management is a very important issue. There are several methods for key management, but most of these are based on Public Key Cryptography, which are typically based on Number Theory. Key Pre-Distribution is an alternative method based on Combinatorics. This method may be used for a scenario where security requirement is not so stringent.
Time: Wednesday, April 20th 3:30-4:30 pm
Location: Duques Hall, Room 553 (2201 G Street NW)
Particle Learning for Fat-tailed Distributions
Speaker: Hedibert Lopes, University of Chicago Booth School of Business
It is well-known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this paper we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a t-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffrey’s prior. We illustrate our sequential methodology on the British pound/US dollar daily exchange rate data and on data from the 2008-2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.
Time: Thursday, April 7th 11:30-12:30pm
Location: Duques 652 (2201 G Street, NW)
The Planning of Guaranteed Targeted Display Advertising
Speaker: John Turner, University of California, Irvine
As targeted advertising becomes prevalent in a wide variety of media vehicles, planning models become increasingly important to ad networks that need to match ads to appropriate audience segments, provide a high quality of service (meet advertisers’ goals), and ensure ad serving opportunities are not wasted. We define Guaranteed Targeted Display Advertising (GTDA) as a class of media vehicles that include webpage banner ads, video games, electronic outdoor billboards, and the next generation of digital TV, and formulate the GTDA planning problem as a transportation problem with quadratic objective. By modeling audience uncertainty, forecast errors, and the ad server’s execution of the plan, we derive sufficient conditions that state when our quadratic objective is a good surrogate for several ad delivery performance metrics. Moreover, our quadratic objective allows us to construct duality-based bounds for evaluating aggregations of the audience space, leading to two efficient algorithms for solving large problems: the first intelligently refines the audience space into successively smaller blocks, and the second uses scaling to find a feasible solution given a fixed audience space partition. Near-optimal schedules can often be produced despite significant aggregation.
Time: Friday, March 25th 3:30-4:30pm
Location: Duques 553 (2201 G Street, NW)
Optimal dispatching models for server-to-customer systems with classification errors
Speaker: Laura A. McLay, Department of Statistical Sciences and Operations Research, Virginia Commonwealth University
How to dispatch servers to prioritized, spatially-located customers is a critical issue in server-to-customer systems. Such decisions are complicated when servers have different operating characteristics, customers are prioritized, and there are errors in assessing customer priorities. This research provides a model for optimizing dispatching protocols using infinite horizon, average cost Markov decision process models. The proposed model determines how to optimally dispatch heterogeneous servers to customers to maximize the long run average utility in a Markov decision process. Our model sheds light on when to dispatch the closest server to a customer and when to dispatch a farther server to a customer. Dispatching is complicated when servers must be both efficiently and equitably dispatched to customers. Four types of equity side constraints are considered that reflect customer and server equity. The equity constraints draw upon the decision analytic and social science literature in order to compare the effects of different notions of equity on the dispatching policies. The model has applications to emergency medical services and military medevacs.
Time: Friday, February 4th 11:30-12:30pm
Location: Duques 553 (2201 G Street, NW)
Optimal Dynamic Return Management of Fixed Inventories
Speaker: Mehmet Altug, Department of Decision Sciences, The George Washington University
While the primary effort of all retailers is to generate that initial sales, return management is generally identified as a secondary issue that does not necessarily need the same level of planning. In this paper, we position return management as a process that is at the interface of both inventory and revenue management by explicitly incorporating the return policy of the retailer in consumer’s valuation. We consider a retailer that sells a fixed amount of inventory over a finite horizon. We assume that return policy is a decision variable which can be changed dynamically at every period. According to a hypothesis which is quite prevalent in the retailing industry, while flexible and more generous return policies increase consumer valuation and generate more demand, they also induce more returns. In this environment, we characterize the optimal dynamic return policies based on two costs of return scenarios. We show a conditional monotonicity result and discuss how these return policies change with respect to retailer’s inventory position and time. We then propose a heuristic and prove that it is asymptotically optimal. We also study the joint dynamic pricing and dynamic return management problem in the same setting and propose two more heuristics whose performance is tested numerically and found to be close to optimal for higher inventory levels. We finally extend our model to multiple competing retailers and characterize the resulting equilibrium return policy and prices.
Time: Wednesday, February 2nd 11:00-12:15 pm
Location: Funger 620