I2SDS Seminars: Spring 2013

The Role of Predictive Distributions in Process Optimization

Quality improvement has been described in a nutshell as “reduction in variation about a target”. Such reduction is driven by the desire to have a high probability of meeting process specifications. However, many statistical quantifications and decisions related to process optimization and response surface analyses are focused only on means, without careful thought to the role of variation and risk assessment. A focus on inference for means is also evident from a review of classical response surface methodology textbooks and popular statistical packages for process optimization.

Vast Search Affect

The American public is often confronted with sensationalized studies that show statistically significant results for one new phenomenon or another. For example, you might read a headline such as this: “Mother’s Depression Linked to Child’s Shorter Height,” ABC News, Sept 10, 2012. The headline is flashy enough to grab attention. But a new level of truth becomes apparent when one takes the time to learn how the study was designed and how the information was analyzed.

Quality of Target Benefits of Proposed Projects: Developing a Generic Construct

Organizational growth is accelerated by successful implementation of projects, hence project selecting and funding are critical organizational decisions. While literature is comprehensive in its discussion on financial analysis of proposed projects, it is weaker when it comes to assessment of non-financial target benefits, those anticipated to be realized after project completion. Consequently, target benefits of proposed projects are often vaguely defined, inflated, and suffer from optimism bias.

Should Event Organizers Prevent Resale of Tickets ?

We are interested in whether preventing resale of tickets benefits the capacity providers for sporting and entertainment events. Common wisdom suggests that ticket resale is harmful to event organizers’ revenues and event organizers have tried to prevent resale of tickets. For instance, Ticketmaster has recently proposed paperless (non-transferrable) ticketing which would severely limit the opportunity to resell tickets.

Adversarial Risk Analysis: Games and Auctions

Adversarial risk analysis is a decision-analytic approach to strategic games. It builds a Bayesian model for the solution concept, goals, and resources of the opponent, and the analyst can then make the choice that maximizes expected utility against that model. Adversarial risk analysis operationalizes the perspective in Kadane and Larkey (1982), and it often enables the analyst to incorporate empirical data from behavioral game theory. The methodology is illustrated in the context of Le Relance, a routing game, and auctions.

Cholesky Stochastic Volatility Models for High-Dimensional Time Series

Multivariate time-varying volatility has many important applications in finance, including asset allocation and risk management. Estimating multivariate volatility, however, is not straightforward because of two major difficulties.

Next Generation of Mathematical Programming Modeling and Solving Tools

During the last 40 years Mathematical Programming (MP) has increasingly found applications in industry in various areas such as finance, marketing, supply chain, energy, data mining and decision analytics. This talk reviews the current state of the art of MP in view of recent developments. Historically, modeling mathematical programming problems has relied on either of two methodologies. The speaker will address these and questions about how today’s companies can implement the next generation of mathematical programming and the benefits they can accrue from it.

Big Data Revolution: Analytics and Optimization

How do companies define Big Data? How are they using it? What strengths are they receiving from Big Data Analytics? We will review what Big Data means, and we will explore case studies from Banking, Insurance, Retail and Healthcare Verticals.

Stable Distributions: Models for Heavy Tailed data

Stable distributions are a class of heavy tailed probability distributions that generalize the Gaussian distribution and that can be used to model a variety of problems. An overview of univariate stable laws is given, with emphasis on the practical aspects of working with stable distributions.

Introduction to R: modeling, computing, visualizing and fun

The use of the R environment for statistical computing and data analysis has exploded over the last decade and R has matured into a mainstream and must-know environment that every serious (and fun loving!) academic should leverage. Given its unrivalled advantages in terms of access, help, flexibility, extensibility and aesthetics, the presentation will showcase the salient features of R so as to provide you with a working knowledge of R.