George Mason University Statistics Faculty

Anand Vidyashankar My research interests span a variety of areas comprising Biostatistics, Statistics, and Probability. The scientific areas in which I have worked are given in this webpage.

RESEARCH INTERESTS:

Biostatistics:

Response Adaptive Designs
Longitudinal Data Analysis
Surival Analysis
Statistical issues in Toxicology
Parasitology

Statistics in Biotechnology:

Statistical Modeling of Polymerase Chain Reaction (PCR) Dynamics
Statistical Issues in Genomic Stability
Statistical Models in DNA Shuffling

Statistical Models for the Internet

Models for Multicast traffic
Modeling and Analysis of "Online Phenomenon"
Cascade Models

Statistics in Finance

Statistical Models in Electronic Banking
Risk Modeling in Credit Union Research
Statistical Analysis of Online Trading Startegies

Data Confidentiality

Synthetic Data
Privacy Risk Identification in Healthcare Data
Spatial Aspects of Privacy Risk

Probability and Stochastic Processes

Large Deviations and Conditional Limit Laws
Uniform Central Limit Theorems
Almost Sure Central Limit Theorems
Branching Processes
Branching Random Walk
Random Walk on Trees

Statistics:
- Minimum Distance Methodology
- Empirical Likelihood
- Inference for Branching Processes
- Inference under Sparsity
- Sequential Estimation and Testing
- Nonlinear Regression
- Semi-linear Regression
- Random Matrices
My Research is Supported by NSF and Industry.

Time dependent covariates in a competing risk setting

Rare Event Simulation for Processes Generated via Stochastic Fixed Point Equations

Tail Estimates for Stochastic Fixed Point Equations via Nonlinear Renewal Theory

Statistical and Biological Considerations in Evaluating Drug Efficacy in Equine Strongyle Parasites using Fecal Egg Count Data

Bayesian Model Robustness via Disparities

Inference for Quantitation Parameters of a Polymerase Chain Reaction via Branching Processes with Random Effects

Weak Convergence Results for Multiple Generations of a Branching Process

Multivariate methods for identifying differentially expressed genes

Multivariate methods for identifying differentially expressed genes-Matlab Code

Asymptotic Inference for High Dimensional Data-Preprint

Inference Concerning Generation Sizes of a BP

Path Properties of Multigenerational Samples from BP

A Novel Approach to Combining in vitro and in vivo data

Statistical Approach Measure Efficacy of Anthelmintic Resistance

Hellinger_distance for RPWD

Doctoral Students

An-lin Cheng, graduated 2004

Serena Chan, graduated 2008

Bret Hanlon, Expected 2009

Post-doctoral Fellows

Josh Rushton, 2005-2007

My Research Page

Recent Tech. Reports

ILRST-717 Rats Program
ILRST-717 Rats Data
ILRST-717 Milk Data
ILRST-717 Protein Content Data Description
Dental Study Data
Documentation on Dental Study Data
Ozone Tree Data
Rats Mixed Analysis VIII
Description of Rats Analysis VIII
Home Work 1

Professor
George Mason University
Statistics
1715, The Engineering Building
GMU, Fairfax, VA

Phone: (703)993-7151
Fax: (703)993-1700
E-Mail: avidyash@gmu.edu

Education:
Ph.D. Statistics and Mathematics, Iowa State University, Ames, Iowa, 1994

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