Seminars

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Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 14th July 2016
11:00am
Multivariate investigations in ecology: The search for significance
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Ecosystems are composed of multiple layers of interactions within and among biotic communities and their environments, leading to multivariate ecological studies. One of the fundamental questions in ecology is why biodiversity arose, and how it is maintained. Namely, why are species in a particular location, why and how do these species persist, and how do these species affect one another? My area of interest lies in trophic interactions, such as predator-prey dynamics, considering the flow of energy among various organisms as one of the main drivers of ecosystem function. Identifying the “true” ecological factors from a suite of possibilities is challenging, thus the tendency to rely on tests that produce a measure of statistical significance. As examples, I will draw on my research experiences with examining sponge composition on Caribbean coral reefs, and the spatial distribution of exploited seahorse populations in Southeast Asia. Because statistical significance does not always denote a significant ecological effect, I combined statistical outcomes with probable or confirmed mechanistic pathways. This allows for a fuller understanding of my study systems, a useful and necessary step for real-world applications, such as ecosystem-based fisheries management.

Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 7th July 2016
4:00pm
Improved nonparametric estimation of the number of zeros and illustrations
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In this talk we present some lower bounds for the probability of zero for the class of count distributions having a log-convex probability generating function, which includes Compound and Mixed Poisson distributions. These lower bounds allow to construct non-parametric estimators of the non-observed number of zeros, which are useful in capture-recapture models. Some of these bounds lead to the well known Chao's and Turing's estimators. Several examples of application are analyzed and discussed.


Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 5th July 2016
11:00am
Vincent Zhai, PhD Student, UBC Statistics
Stochastic Processes, Statistical Inference and Efficient Algorithms for Phylogenetic Inference
Show Abstract
Phylogenetic inference aims to reconstruct the evolutionary history of populations or species. With the rapid expansion of genetic data available, statistical methods play an increasingly important role in phylogenetic inference. In this talk, we present new evolutionary models, statistical inference methods and efficient algorithms for reconstructing phylogenetic trees at the level of populations using single nucleotide polymorphism data and at the level of species using multiple sequence alignment data.
 
At the level of populations, we introduce a new inference method to estimate evolutionary distances for any two populations to their most recent common ancestral population using single-nucleotide polymorphism allele frequencies. Our method is based on a new evolutionary model for both drift and fixation. To scale this method to large numbers of populations, we introduce the asymmetric neighbor-joining algorithm, an efficient method for reconstructing rooted bifurcating trees. 
 
At the level of species, we introduce a continuous time stochastic process, the geometric Poisson indel process, that allows indel rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor-joining.


 

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Department of Statistics

Department of Statistics, University of British Columbia
3182 Earth Sciences Building
2207 Main Mall
Vancouver, BC, Canada V6T 1Z4
Tel: 604.822.0570
Fax: 604.822.6960

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