The transmission of voice-over-Internet protocol (VoIP) network traffic is used in an increasing variety of applications and settings. Many of these applications involve communications where VoIP systems are deployed under unpredictable conditions with poor network support. These conditions make it difficult for users to configure and optimize VoIP systems and this creates a need for self confi...

This paper studies the identification and estimation of the first-price auction model with risk averse bidders within the private value paradigm. We show that the benchmark model is nonidentified in general from observed bids. We then consider various extensions including a binding reserve price, affiliation among private values and asymmetric bidders. In particular, we exploit heterogeneity ac...

In a survey disclosure model, we consider an additive noise privacy mechanism and study the trade-off between privacy guarantees and statistical utility. Privacy is approached from two different but complementary viewpoints: information and estimation theoretic. Motivated by the performance of principal component analysis, statistical utility is measured via the spectral gap of a certain covari...

We consider the estimation of dynamic binary choice models in a semiparametric setting, which per-period utility functions are known up to finite number parameters, but distribution shocks is left unspecified. This setup differs from most existing identification and literature for discrete models. To show we derive exploit new recursive representation unknown quantile function shocks. Our estim...

My core statistical research focuses on semiparametric/nonparametric methodology and large sample theory — efficient estimation in semiparametric models, nonparametric function estimation (with emphasis on shape constrained estimation), and bootstrap based inference in non-standard problem. I am also actively involved in interdisciplinary research in astronomy. My research has applications in b...

This paper considers estimation of normal mean ? when the variance is unknown, using the LINEX loss function. The unique Bayes estimate of ? is obtained when the precision parameter has an Inverse Gaussian prior density