Room 4192, Earth Sciences Building, 2207 Main Mall
Tue 22nd April 2014
Model and Inference Issues Related to Exposure-Disease Relationships
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We work on two issues related to exposure-disease relationships. Firstly we build a Bayesian hierarchical model for relating disease to a potentially harmful exposure, using data from studies in occupational epidemiology, and compare our method with the traditional group-based exposure assessment method through simulation studies, a real data application, and theoretical calculation. We focus on cohort studies where a logistic disease model is appropriate and where group means can be treated as fixed effects. The results show a variety of advantages of the fully Bayesian approach, and provide recommendations on situations where the traditional group-based exposure assessment method may not be suitable to use.
Secondly the shape of the relationship between a continuous exposure variable and a binary disease variable is often central to epidemiologic investigations. We investigates a number of issues surrounding inference and the shape of the relationship. Presuming that the relationship can be expressed in terms of regression coefficients and a shape parameter, we investigate how well the shape can be inferred in settings which might typify epidemiologic investigations and risk assessment. We also consider a suitable definition of the average effect of exposure, and investigate how precisely this can be inferred. This is done both in the case of using a model acknowledging uncertainty about the shape parameter and in the case of using a simple model ignoring this uncertainty. We also examine the extent to which exposure measurement error distorts inference about the shape of the exposure-disease relationship. All these investigations require a family of exposure-disease relationships indexed by a shape parameter. For this purpose, we employ a family based on the Box-Cox transformation.
Room 4192, Earth Sciences Building 2207 Main Mall, UBC
Tue 15th April 2014
PhD Candidate, Dept. of Statistics, UBC
Hypothesis testing under density ratio models in the presence of Type I censored multiple samples
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Maintaining a high quality of lumber products is of great social and economic importance. As part of a research program aimed at developing a long term program for monitoring change in the strength of lumber, we develop a theory of hypothesis testing concerning a given number of populations with Type I censored samples from each. Statistical methods for lumber quality monitoring should ideally be efficient and nonparametric. These desiderata lead us to adopt a semiparametric density ratio model to pool the information across multiple samples and use the nonparametric empirical likelihood (EL) as the tool for statistical inference. We establish a powerful framework for performing EL inference under the density ratio model when Type I censored samples are present. This inference framework centers on a concave dual partial EL, and features an easy computation. We find that under a class of general composite hypotheses, the corresponding EL ratio test has a classical chi--square limiting distribution under the null model and a non--central chi--square limiting distribution under local alternatives. We show that the local power of this EL ratio test is often increased when strength is borrowed from additional samples even when their underlying distributions are unrelated to the hypothesis of interest. Simulation studies show that this test has better power properties than all potential competitors adopted to the multiple sample problem under the investigation, and is robust to model misspecification. The proposed test is then applied to assess strength properties of lumber with intuitively reasonable implications for the forest industry.
Room 1013, Earth Sciences Building 2207 Main Mall, UBC
Thu 10th April 2014
The Lasso: a brief review and a new significance test
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I will review the lasso method and show an example of its utility in
cancer diagnosis via mass spectometry.
Then I  will  consider the testing the significance of the terms in a
fitted regression, fit via the lasso.
I will present a novel test statistic for this problem, and show that
it has a simple asymptotic  null distribution.
This work builds on the least angle regression approach for fitting the lasso, 
and the notion of degrees of freedom  for adaptive models (Efron 1986) and 
for the lasso (Efron et. al 2004, Zou et al 2007).
We give examples of this procedure, discuss extensions to generalized
linear models and the Cox model, and describe an R language package
for its computation.

This work is joint with Richard Lockhart (Simon Fraser University),
Jonathan Taylor (Stanford Univ) and Ryan Tibshirani (Carnegie Mellon University)

Room 4192, Earth Science Building, 2207 Main Mall, Vancouver
Tue 1st April 2014
University of Otago, New Zealand
Phylogenetic analysis of species radiations using SNPs and AFLPs. [Bio/Bioinformatics/Genetics]
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Technological wonders such as next generation sequencing mean that we can now, in principle, obtain SNP (single nucleotide polymorphism) data from multiple individuals in multiple species. This promises enormous benefits for population genetic and phylogenetic analysis, particularly of closely related or poorly resolved species. My interest is in how to analyse these data effectively and responsibly. We have developed an algorithm which estimates species trees, divergence times, and population sizes from independent (binary) makers such as well spaced SNPs. The method is based on coalescent theory (like the BEAST software), though it uses mathematical trickery to avoid having to consider all the possible gene trees. As a `full likelihood' method, it should be more accurate than alternative FST based approaches. I'll talk about our experiences applying this method to AFLP data from alpine plants, and some recent discoveries about the usefulness (or uselessness) of SNP data for estimating population sizes.

Bio: David did his PhD with Mike Steel at the University of Canterbury. He had postdocs with David Sankoff (Montreal) and Olivier Gascuel (Montpellier), before taking up a position at McGill. After tenure, he moved back to NZ for positions at the University of Auckland, and more recently Otago, which is in Dunedin in the South Island of NZ.

a place of mind, The University of British Columbia

Department of Statistics

Department of Statistics, University of British Columbia
3182 Earth Sciences Building
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Vancouver, BC, Canada V6T 1Z4
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Fax: 604.822.6960

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