The SSC Comes To Town

After several months of waiting, the program for the Annual Meeting of the Statistical Society of Canada (SSC) has been released. I know – I’m excited too.

For those not in the know, the annual meeting of the SSC will be held at the University of Guelph this year. To be more specific, the event will run from June 3 until June 6. I’m looking forward to chatting with colleagues from other universities, catching up with old friends, and listening to talks about probability theory, biostatistics, epidemiology, spatial statistics, and more.

Of course, I’m also excited that I will be presenting some of my research. My talk – titled Signal Processing for Species Identification – is scheduled for June 6.

However, what makes the 2012 Annual Meeting of the SSC even better is that 1 of my graduate students (Kathleen Ryan), 1 of my friends and collaborators (Lorna Deeth), my undergraduate research assistant (Justin Angevaare), and another undergraduate student who has been working with some of my data (Matthew Rueffer) are all involved. Lorna will be presenting some of her Ph.D. research on Tuesday June 5. Justin, Kathleen, and Matt are all presenting posters during the second poster session, also on June 5.

For those of you that might be interested, I’ve included the respective abstracts for each of these presentations below.

Anyway, the SSC begins in less than a month. If you haven’t registered yet, you can do so by clicking here.


The Utility of Catch Per Unit Effort Variance
Justin Angevaare, Daniel Gillis
Tuesday June 5, 1:15


Population models play a central role in fisheries management. Commercial harvest data, especially catch per unit effort (CPUE) are used to predict population parameters and abundance. A survey of recent literature reveals that CPUE variability is typically ignored. The common practice is to aggregate harvest and effort by year. We present a simulation study to explore traditional models and those that have been modified to include CPUE variability. Daily fish populations and harvest events are simulated over 60 years. Catch and effort are aggregated over several temporal scales. Population level parameter estimates are compared across models and temporal aggregation scales.


Depth Distribution of Larval Lake Whitefish Coregonus clupeaformis in a Great Lakes Embayment
Kathleen Ryan, Daniel Gillis, Stephen Crawford
Tuesday June 5, 1:15

This study investigated the relationship between larval lake whitefish (Coregonus clupeaformis) density and water depth in Stokes Bay, Lake Huron. Nearshore waters (1-3m) of the Great Lakes have been hypothesized to provide favourable habitat for larval whitefish, leading investigators to focus survey work there. The distribution of larvae in Stokes Bay was investigated by surface tows of 500 micron nets between April and May 2011. A stratified random sampling design assigned sample sites over depths ranging from 1-11m (n=71). Statistical analyses suggest depth was not significant in predicting larval fish density.


Spatial and Temporal Modelling of Change in Marine Harvests
Matthew Rueffer, Julie Horrocks, Daniel Gillis
Tuesday June 5, 1:15

From 1979 until 2010, catch data on whitefish (Coregonus clupeaformis) has been collected for the Canadian portion of Lake Huron. This data, obtained from native fishers and the Ontario Ministry of Natural Resources, contains the geographic location, harvest, relative effort and mesh size. This poster will examine catch per unit effort (CPUE – the quotient of harvest and effort) across regions and years, using exploratory techniques, temporal and spatial models.


Model Choice Using the Deviance Information Criterion for Latent Conditional Individual-Level Models of Infectious Disease Spread
Lorna Deeth, Rob Deardon, Daniel Gillis
Tuesday June 5, 4:00


Individual-level models (ILMs) are a class of complex, statistical models that are often fitted within a Bayesian framework, and which can be suitable for modeling infectious disease spread. The deviance information criterion (DIC) is a model comparison tool that is appropriate for complex, Bayesian models, and since its development a number of variants have been proposed, including those for its application to missing data models. Here, we assess five variants of the DIC and their application to ILMs, in particular a class of infectious disease models known latent conditional individual-level models, which display mixture model-like characteristics due to their dependence on a latent grouping variable. The effectiveness of the traditionally defined DIC is compared to alternative DIC definitions through a simulation study, to assess which is most applicable for this class of models. Epidemic data is generated under a latent conditional ILM, to which both a spatial ILM and the latent conditional ILM are fitted. Each variant of the DIC is then calculated for every fitted model, and the DIC values obtained for the latent conditional ILM are compared to those from the spatial ILM.


Signal Processing for Species Identification
Daniel Gillis
Wednesday June 6, 11:15


Industrial processes often require water intake from the Great Lakes for cooling purposes. This process has the side effect of entraining fish and fish larvae from various species; the list of which is not fully known. A method for analyzing an unlabelled biological mash sampled from intake reservoirs is presented. The method uses Bayesian signal processing to classify biological markers within the mash against a known marker database. A simulation study is presented to assess the robustness of the model.

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