Chuanshu Ji

Probability Approximations Schemes for Generalized Black-Scholes Pricing Formulas

Asset pricing and volatility modeling take a center stage in financial econometrics. This talk introduces a new method that helps calibration of stochastic volatility models via Markov chain Monte Carlo (MCMC) Bayesian inference based on returns and option data. With the presence of high-dimensional latent volatility processes, numerical integration for computing option prices is required at every time point and every iteration of MCMC.

Thursday, October 4, 2012 - 3:30pm
Type: 

Andreas Artemiou

SAMSI

Utilizing machine learning for sufficient dimension reduction

Sufficient dimension reduction (SDR) ideas are used for supervised dimension reduction in regression problems. Support Vector Machine (SVM) algorithms belong to the class of machine learning techniques which are used for classification. In this talk we discuss Principal Support Vector Machine (PSVM) a method which utilizes SVM to achieve sufficient dimension reduction. PSVM has several advantages over existing methodology for sufficient dimension reduction, with the most important one being the fact that we can do linear and nonlinear dimension reduction under a unified framework.

Thursday, September 27, 2012 - 3:30pm
Type: 
306 Statistics Building

James Livsey

Clemson University

Small integer time series via discrete renewal processes

Discrete renewal processes are ubiquitous in stochastic phenomenon. In this talk constructing a discrete process where renewals are more (or less) likely during specified seasons is of specific interest. For example thunderstorms in the Southern United States can take place at any time in the year, but are most likely during the summer. Hurricanes, tornadoes, and snowstorms are other meteorological count processes obeying periodic dynamics. Rare disease occurrences, accidental deaths, and animal sightings are non-meteorological examples of count phenomenon following a periodic structure.

Thursday, September 20, 2012 - 3:30pm
Type: 
Room 306, Statistics Building

Martin Klein

US Census Bureau

Statistical Analysis of Noise Multiplied Data Using Multiple Imputation

A statistical analysis of data that have been multiplied by randomly drawn noise variables in order to protect the confidentiality of individual values has recently drawn some attention (Nayak, Sinha, and Zayatz, 2011; Sinha, Nayak, Zayatz, 2012). If the distribution generating the noise variables has low to moderate variance, then noise multiplied data have been shown to yield accurate inferences in several typical parametric models under a formal likelihood based analysis (Klein, Mathew, and Sinha, 2012).

Thursday, September 13, 2012 - 3:30pm
Type: 
306 Statistics Building

Robert Gould

The Citizen Statistician

Introductory statistics is in need of a radicalreconceptualization. This need comes from changes to our culture and from revolutionary changes in technology. We propose a new model for introductory statistics that aims to produces citizen statisticians-- citizens capable of critically engaging with data.

Type: 
Thursday, September 6, 2012 - 3:30pm
Room 306, Statistics Building

Rebecca Doerge

Modeling Next-Generation Sequencing Data and Related Statistical Issues

This is an exciting and influential time for the field of Statistics in science. Technological advances in genetic, genomic, and the other 'omic sciences are providing large amounts of complex data that are presenting a number of challenges for the biological community. Many of these challenges are deeply rooted statistical issues that involve experimental design.

Thursday, August 30, 2012 - 3:30pm
Type: 

Sungkyu Jung

Asymptotics for High Dimension, Low Sample Size data and Analysis of Data on Manifolds

This talk consists of two research topics regarding modern non-standard data analytic situations. In particular, data under the High Dimension, Low Sample Size (HDLSS) situation and data lying on manifolds are analyzed. These situations are related to the statistical image and shape analysis.

Thursday, August 23, 2012 - 3:30pm
Type: 

Christopher David O'Neal

PhD Candidate, Statistics

Asymptotic Expansions of Processes with Extreme Value Random Variable Innovations
.

Recently there has been an interest in asymptotic expansions of the tail probabilities of a variety of processes that are ubiquitous in statistics. However, little to no work has been done when the AR(1) process is built upon extreme value random variables. This process appears when the distribution of the current maximum is dependent on the previous. The goal of this dissertation is to explore asymptotic expansions of tail probabilities on this topic, in particular using the Gumbel distribution.

Major Professor(s): 
William P. McCormick & Lynne Seymour
Friday, July 13, 2012 - 3:30pm
Type: 
Room 306, Statistics Building

Cong Feng

PhD Candidate, Statistics

Nonparametric Analysis of Complex Time Series
Complex time series with features, such as non-linearity, high-dimensionality and functional structures, have inspired many interests in statistics community due to limitations of traditional time series models and advancement of methodology and theory of nonparametric statistics. In this dissertation, the nonparametric models for such complex time series are studied.

Complex time series with features, such as non-linearity, high-dimensionality and functional structures, have inspired many interests in statistics community due to limitations of traditional time series models and advancement of methodology and theory of nonparametric statistics. In this dissertation, the nonparametric models for such complex time series are studied. For modeling the financial volatility, we proposed estimators for semiparametric GARCH models with additive autoregressive components linked together by a dynamic coefficient based on spline smoothing.

Major Professor(s): 
Lily Wang & Lynne Seymour
Monday, July 16, 2012 - 3:30pm
Type: 
Room 306, Statistics Building

Yijie Xue

PhD Candidate, Statistics

Applications of Empirical Likelihood to Nonresponse Problem and Changepoint Detection

In this dissertation, I propose an empirical likelihood based method to solve the nonresponse problem and changepoint detection problem. Both methods avoid potential model misspecification problems from which existing parametric methods may suffer. Moreover, the proposed imputation method can correct the bias of the estimate of the complete data for distributions with under- or over-dispersion problem. And the empirical likelihood changepoint detection method is able to detect the change in parameters other than the population mean.

Major Professor(s): 
Nicole Lazar
Type: 
Monday, July 16, 2012 - 10:00am
Room 307, Statistics Building

Pages

Subscribe to RSS - Colloquium