This paper looks at the post-financial-crisis period, and the relationship between default risk and corporate governance for financial firms outside North America. Default risk is measured by both credit default swap spreads (CDS) and the Merton-type default probabilities from Bloomberg. Our...

We examine auction design in a context where symmetrically informed buyers and sellers of a good with a common but uncertain value learn through experience. Buyer strategies, even in the very long run, do not converge to the Bertrand-Nash strategy of bidding the expected value of the good....

Purpose Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new...

We investigate the importance of board expertise by analyzing the role of “directors from related industries” (DRIs) on a firm's board. DRIs are officers and/or directors of companies in the upstream (supplier) or downstream (customer) industries of the firm. About 40% of firm-years in our...

We investigate the importance of board expertise by analyzing the role of “directors from related industries” (DRIs) on a firm's board. DRIs are officers and/or directors of companies in the upstream (supplier) or downstream (customer) industries of the firm. About 40% of firm-years in our...

This dissertation presents two topics from opposite disciplines:one is from a parametric realm and the other is based on nonparametric methods.The first topic is a jackknife maximum likelihoodapproach to statistical model selection and the second one is a convexhull peeling depth approach to...

Regression is the procedure that attempts to relate a$p$-dimensional vector of predictors $Xb$ with a response variable$Y$. Frequently, we deal with regression problems that have a largeamount of predictors. In those cases, we try to reduce thedimension of our predictor vector. The reason we are...

Parameter identifiability is very useful if one wishes to make inferences in a statistical model. There are two important nonidentifiabilities in finite mixture models : boundary nonidentifiability and label nonidentifiability. Although parameters are not identifiable in the strict sense, in...