Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a nonlinear fashion, such as when the response is binary. Although this modeling problem has been extensively studied, it remains unclear how to effectively control the fraction of false discoveries even in high-dimensional logistic regression, not to mention general high-dimensional nonlinear models.

To cook a delicious dish, three (3) components are required—good cooking materials, good kitchen cookware, and good cooking skills. This is the same as doing a Big Data research. To do a good job in Big Data analysis, a reliable data is your material, a powerful computer is your cookware, and the most important cooking skill is in fact the statistical methodology. It is worth mentioning that the cooking material and cookware are buyable, if you are willing to pay. However, the cooking skill (Statistical thinking and methodology) needs to be learnt by yourself.

Identification through heteroskedasticity in heteroskedastic simultaneous equations models (HSEMs) is considered. The possibility that heteroskedasticity identifies the structural parameters only partially is explicitly allowed for. The asymptotic properties of the identified parameters are derived. Moreover, tests for identification through heteroscedasticity are developed and their asymptotic distributions are derived. Monte Carlo simulations are used to explore the small sample properties of the asymptotically valid methods.

Start-up companies play an important role in innovation and many of them have become the leaders of their fields. With limited capital and human resources, start-up companies face a range of challenges in managing their research and development (R&D) projects. This presentation covers three topics in relation to R&D project management. First, we consider how to design appropriate project selection standard in a principal -agent model where the agent prefers to maximize the subsidiary profit but the headquarters prefers to maximize the consolidate profit.

Social media platforms for healthcare services are changing how patients choose doctors. The digitization of healthcare reviews has enabled patients to thoroughly evaluate doctors before booking an appointment, and has increased the transparency of the relationship between patients and doctors. In this paper, we wish to derive the impact of online information on patient choice of outpatient care doctors. We are especially interested in how operational factors influence demand.

We create a quality value chain network concept to analyze the impact of supply chain quality (SCQ) on the customer lifetime value (CLV). We apply our framework to a rich dataset from a major restaurant chain utilizing text analysis of the complaints to measure SCQ, a two-stage least squares (2SLS) model with instruments to assess the impact of SCQ on customer experience, and a structural model of consumer purchasing behavior to eventually link customer experience to CLV.

We consider the dynamic pricing and rationing policy of a firm facing strategic customers under the influence of shortage effect. We provide conditions under which it is optimal for the firm to ration. We also identify the necessary and sufficient conditions for the existence of steady state. We also characterize the firm’s pricing and rationing policy under this steady state.

The issue of allocating capacity to accommodate emergent surgery cases while scheduling elective patients has major policy implications for Level-1 trauma centers including most large academic medical centers. This is because operating rooms (ORs) are the greatest source of revenues for hospitals, while also being the largest cost centers. However, scheduling ORs, especially at level-1 trauma hospitals, is challenging due to significant uncertainty in the arrivals of patients requiring emergent surgery.

The introduction of on-demand ride-hailing platforms totally changed the way people commute. In recent years, several firms entered this market to directly compete with traditional taxi companies. Some of these online platforms offer a carpooling service in which several passengers heading in the same direction can share a ride by being efficiently matched to an available vehicle. Examples of such services in NYC include uberPOOL, Lyft Line and Via.

Portfolio risk measurement under the nested setting is a challenging computational problem, and has received increasing attention in recent years. This nested setting often requires mark-to-market reevaluation of the portfolio for a large number of possible scenarios of risk factors up to a future time horizon. When closed-form formula is not available, reevaluation may require intensive simulations that are time consuming. This paper aims to develop a new simulation method that is computationally efficient for measurement of conditional Value-at-Risk (CVaR) for the portfolio.