Current students

Marian Farah, PhD student in Statistics and Stochastic Modeling

Kassie Fronczyk, PhD student in Statistics and Stochastic Modeling

Valerie Poynor MSc student in Statistics and Stochastic Modeling

Ziwei Wang PhD student in Statistics and Stochastic Modeling (co-advised with Abel Rodriguez)

Ph.D. alumni

Matthew Taddy, Assistant Professor of Econometrics and Statistics, University of Chicago, Graduate School of Business.
Ph.D. in Statistics and Stochastic Modeling, Spring 2008, School of Engineering, UCSC.
Dissertation title: Bayesian Nonparametric Analysis of Conditional Distributions and Inference for Poisson Point Processes.

In his PhD thesis work, Matt studied a flexible approach to Bayesian nonparametric modeling and inference
for conditional densities, including development of novel modeling frameworks for fully nonparametric
quantile regression, multivariate regression for survival data, and semiparametric Markov switching regression.
Moreover, Matt developed a general modeling framework for spatial Poisson processes, including methods for
regression with individual-specific covariates (marks) and location-specific covariates, as well as modeling for
spatial point patterns that are observed over discrete time. Related papers include:

Taddy, M., and Kottas, A. ``A Bayesian Nonparametric Approach to Inference for Quantile Regression.''
To appear in Journal of Business and Economic Statistics. (earlier version available as AMS Tech Report 2007-21)
Taddy, M., and Kottas, A. ``Markov Switching Dirichlet Process Mixture Regression.''
To appear in Bayesian Analysis. (earlier version available as UCSC-SOE Tech Report 2008-15)

Matt was also involved in a collaborative project on statistical modeling and sensitivity analysis for
radiative transfer computer models. Related references include:

Morris, R.D., Kottas, A., Taddy, M., Furfaro, R., and Ganapol, B.D. (2008). ``A Statistical Framework
for the Sensitivity Analysis of Radiative Transfer Models.''
IEEE Transactions on Geoscience and Remote Sensing, 46, 4062-4074.
Morris, R.D., Kottas, A., Furfaro, R., Taddy, M., and Ganapol, B. (2007). ``An Analysis of the
Uncertainties in Radiative Transfer Models Used in Remote Sensed Data Product Generation.''
Proceedings of the NASA Science Technology Conference, June 2007.

Milovan Krnjajic, Stokes Lecturer in Statistics, School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway.
Ph.D. in Computer Science, Summer 2005, School of Engineering, UCSC.
(PhD dissertation work co-supervised with David Draper).
Dissertation title: Contributions to Bayesian Statistical Analysis: Model Specification and Nonparametric Inference.

For the part of his PhD thesis that involved Bayesian nonparametrics, Milovan worked on Bayesian
semiparametric methodology for quantile regression, developing Dirichlet process mixture models for
the error distribution in an additive quantile regression formulation, including dependent Dirichlet
process modeling for quantile regression error densities that change nonparametrically with the covariates.
Milovan also studied several classes of nonparametric models (based on Dirichlet process priors) for
count data that arise in treatment/control experiments. Related references include:

Kottas, A., and Krnjajic, M. (2009). ``Bayesian Semiparametric Modelling in Quantile Regression.''
Scandinavian Journal of Statistics, 36, 297-319. (earlier version available as AMS Tech Report 2005-06)
Krnjajic, M., Kottas, A., and Draper, D. (2008). ``Parametric and Nonparametric Bayesian
Model Specification: A Case Study Involving Models for Count Data.''
Computational Statistics & Data Analysis, 52, 2110-2128.

More recently, we have been working on model-based nonparametric regression approaches that
combine nonparametric prior models for the regression function and the error distribution. Some
early results in the context of quantile regression are reported in

Kottas, A., Krnjajic, M., and Taddy, M. (2007). ``Model-based approaches to nonparametric
Bayesian quantile regression.''
ASA proceedings of the 2007 Joint Statistical Meetings, IMS, pp. 1137-1148.

M.Sc. alumni

Joel Mefford, M.Sc. in Computer Science, Fall 2005, School of Engineering, UCSC.
Thesis title: Bayesian Nonparametric Mixtures of Weibull Distributions With Applications to Survival Analysis.

Elizabeth Pacheco, M.Sc. in Statistics and Stochastic Modeling, Winter 2009, School of Engineering, UCSC.
(MS project co-supervised with Bruno Sanso).
Project title: Bayesian Modeling Approaches for Marked Non-Homogeneous Poisson Processes.

thanos@ams.ucsc.edu

Last updated October 29, 2009