June 25, 2018

"A Celebration of Statistics"

April 29,2010
Graduate Student Research Day

Organizing Committee:
Statistics Graduate Student Union
Department of Statistics


Speaker Abstracts

Yan Bai, Ph.D. Graduate, Department of Statistics
Region Adaptive algorithm with Online Recursion

The efficiency of Markov chain Monte Carlo (MCMC) algorithms can vary dramatically with the choice of simulation parameters. Adaptive MCMC (AMCMC) algorithms allow the automatic tuning of the parameters while the simulation is in progress. In the case of the target with multimodes, we propose an adaptation process which involves fitting the mixture using the available samples via an online EM algorithm and, based on the current mixture parameters. The method is compared with other regional AMCMC samplers and is tested on simulated as well as some examples.


Billy Chang, Ph.D. Candidate, Department of Public Health Sciences
Statistical Approach to Transcript Quantification in RNA-Seq

Recently developed Next Generation Sequencing techniques, such as the RNA-Seq protocol, provide unprecedented precision and throughput for transcriptome analysis. The data generative mechanism implied by RNA-Seq however differs substantially from its predecessors, such as microarrays. This calls for new methodological development for analyzing data created under the new sequencing protocol. In this talk I will focus on the problem of transcript quantification, i.e. how to quantify the amount of transcripts within a sample of DNA from a molecular/cell content. After a brief introduction to the RNA-Seq sequencing pipeline, I will discuss some of the challenges involved in transcript quantification under the RNA-Seq protocol, and how statistical methods may resolve the problems at hand.


Madeleine Thompson, Ph.D. Candidate, Department of Statistics
Using Correlation Length to Compare MCMC Methods Graphically

Comparisons of Markov Chain Monte Carlo methods that use tables of figures of merit are limited by the number of simulation results
that can be displayed without overwhelming the reader. I will demonstrate a method for comparing MCMC methods that uses grids of plots of estimated correlation length to communicate the results of a diverse collection of simulations in a way that readers can interpret easily, enabling them to more easily identify an MCMC method suitable for a given task.

Angel Valov, PhD Graduate, Department of Statistics, University of Toronto
First Passage Times for Brownian Motion with Applications

The first time a continuous stochastic process crosses a given, time-dependent, boundary function is known as a first-passage time (FPT) for the process. FPTs give rise to stopping time problems which have a long history and which have recently received renewed attention from both industry and academia due to their applicability in finance. In this talk I will briefly outline the classical FPT problems for Brownian motion as well as discuss a more recent variation of the original problems. In addition, I will present some 'real world' applications as well as sketch a number of potential applications from finance and statistics.