I have been tackling some "big picture" questions related to using statistical methods in practice.
My major focus is on assessing the differences between explanatory, predictive and descriptive modeling and statistical modeling in terms of the statistical modeling process (from data collection and goal definition to model use). My paper To Explain or To Predict? discusses the distinction from a statistical point of view. The paper Predictive Analytics in Information Systems Research examines the value of predictive modeling to theory building, testing, and validation, illustrated in information systems research which is monopolized by explanatory modeling.
During my Ph.D. I developed a method for computing exact probabilities for random variables that arise when runs or scans are used. A run is a sequence of consecutive successes in a series of Bernoulli trials. A scan is a “window” of consecutive Bernoulli trials that includes at least a given number of successes. Runs and scans are applied in various fields. Although they are easy to understand and use, the random variables that arise tend to have characteristics (e.g. probability functions, moments) that are complicated for computation.
Shmueli et al. (2005) revived a useful discrete distribution called the COM-Poisson (the Conway–Maxwell–Poisson) and introduced its statistical and probabilistic properties. This distribution is a two-parameter extension of the Poisson distribution that generalizes some well-known discrete distributions (Poisson, Bernoulli and geometric).
Empirical research of online auctions such as eBay has been dominated by researchers from economics and information systems. Together with colleagues from Information Systems and statistics, I have been working on developing statistical methods for visualizing, collecting, modeling, predicting and analyzing such data. Bid data (and other types of eCommerce data) have non-standard structures and therefore require careful and specialized methods.
Many studies use quasi-experiments, which are similar to randomized experiments except that subjects are not randomly assigned to the treatment and control groups. The result is what's called "self-selection bias", which requires special analysis correction for valid inference about the treatment effect.
Next week I'll be headed to SAS headquarters in Cary, NC for an interview on Analytically Speaking. I look forward to Anne Milley's thought-provoking questions! The announcement promises to walk through several of my research areas:
My paper (with discussion by 4 sets of authors and a rejoinder) that just came out in Quality Engineering, is aimed at introducing the community of industrial statisticians to the different challenges, opportunities and issues in analyzing "Big Behavioral Data" (BBD).
During our Chinese New Year Banquet this week, I received an award for publishing in a top journal: The E.SUN Academic Award (see news coverage). This generous award, established by E.SUN bank, is given to faculty at the top four management schools in Taiwan who publish in one of the few top journals in their field. NTHU joined the program in 2015.