Minimization with respect to a matrix X subject to orthogonality constraints X'X = I has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, the total energy minimization in electronic structure calculation, sparse principal component analysis, community detection and matrix rank minimization, etc. These problems are generally difficult because the constraints are not only non-convex but also numerically expensive to preserve during iterations. This talk will present a few recent advance for solving these problems.

We provide a theory that unifies the long tail and blockbuster phenomena. Specifically, we analyze a three-stage game where, first, a large number of potential firms make entry decisions, then those who stay in the market decide on the investment in its product, and lastly customers with heterogeneous preferences arrive sequentially to make purchase decisions based on product quality and historic sales under the network effect.

I will give a very broad history of efforts to find a method for discovering causal relations, from Aristotle to contemporary work in "artificial intelligence." I will also talk about some of the open problems and difficulties that remain.

Common wisdom suggests that everything else being equal, seeing patients sooner rather than later is preferable. In particular health outcomes improve with reduced delay and so does patient satisfaction. At the same time, if the delay in access to care is reduced, patients may be more inclined not to show up for their appointments given that rescheduling will not result in excessive wait. We investigate how an outpatient care provider should manage capacity in the presence of such strategic behavior of patients.

• Understanding how the context of a choice and heuristics can drive behaviors and actions as much as the attributes of the services or products being offered.
• Introducing a framework to harnesses the power of defaults, message framing, habits and context effects to guide consumers along their decision journey.
• Making intuitive choices easier and desirable behavior the path of least resistance for consumers.

In this paper we propose a model which includes both a known (potentially) nonlinear parametric component and an unknown nonparametric component. This approach is feasible given that we estimate the finite sample parameter vector and the bandwidths simultaneously. We show that our objective function is asymptotically equivalent to the individual objective criteria for the parametric parameter vector and the nonparametric function.

The 2nd International Conference on Econometrics and Statistics (EcoSta 2018) will take place at the City University of Hong Kong, Hong Kong 19-21 June 2018.

The 1st International Conference on Econometrics and Statistics, EcoSta 2017 has taken place at the Hong Kong University of Science and Technology, Hong Kong 15-17 June 2017, and gathered over 650 participants.

Longitudinal functional data consist of functional data collected at multiple time points for which the observational times may vary by subject. They differ from traditional longitudinal data in that the observation at each time point is a function rather than a scalar. The aim is to extend the traditional linear mixed-effects model for longitudinal data to longitudinal functional data.

Since the underlying economic structure is likely to be affected by changes in preferences, technologies, policies, crises, etc., data in the previous time period may be irrelevant to the present data-generation process. Thus, econometric forecasts are often based on rolling estimation. However, it is far from clear how to choose an optimal sample to estimate a predictive model.

Choosing the number of mixture components remains a central but elusive challenge. Traditional model selection criteria can be either overly liberal or conservative when enforcing parsimony. They may also result in poorly separated components of limited practical use. Non-local priors (NLPs) are a family of distributions that encourage parsimony by enforcing a separation between the models under consideration. We formalize NLPs in the context of mixtures and show how they lead to well-separated components that are interpretable as distinct subpopulations.