In the presence of covariate measurement error, there has been extensive interest in developing estimation methods for parameters associated with various survival ...

We introduce novel regression extrapolation based methods to correct the often large bias in subsampling variance estimation as well as hypothesis testing for spatial point and marked point processes.

We propose a multiple estimation adjustment (MEA) method to correct effect overestimation due to selection bias from a hypothesis-generating study (HGS) in pharmacogenetics. MEA uses a hierarchical ...

Phylogenetic trees can improve the power of comparative sequence analyses by placing raw sequence differences into their historical context — offering an understanding of how the sequences that we see ...

Covers discrete and continuous probability laws, random variables; expectations; laws of large numbers and central limit theorem; estimation, testing hypothesis, analysis of variance, regression ...

Generally, the use of ordinary missing data estimation, the UNTIE transformation, and the UNTIE= a-option should be avoided, particularly with hypothesis tests. With these options, parameters are ...

This online data science specialization is designed to provide you with a solid foundation in probability theory in preparation for the broader study of statistics. The specialization also introduces ...

The course provides a precise and accurate treatment of statistical ideas, methods and techniques. Topics covered are sampling distributions of statistics, point estimation, interval estimation, ...

Abstract: This paper offers a new approach to estimate countries’ de facto exchange rate regimes, a synthesis of two techniques. One is a technique that the authors have used in the past to estimate ...

B. Cooper Boniece is an assistant professor in the Department of Mathematics whose research areas are in mathematical statistics and applied probability. He obtained his Ph.D. in 2019 at Tulane ...