Seminar Schedule in Google Calendar
Room 4192 Earth Sciences Building, 2207 Main Mall
Thu 27th November 2014
Professor of Biostatistics
University of Auckland, New Zealand.
Estimation under nearly-correct models
When additional variables are measured on a subsample from an existing cohort, we can fit models using survey-sampling approaches. In some settings we can also estimate by semiparametric maximum likelihood, profile likelihood, or similar techniques, leading to a semi parametric efficient estimator. For example, under case-control sampling we can use weighted logistic regression or unweighted logistic regression. The common wisdom is that the weighted estimators are inefficient because of the variation in the weights. I will argue that this is neither true nor helpful. By considering contiguous model misspecification I will show that the efficient estimator gains its extra precision from relying more heavily on the model, and this is true in a quantitative sense, not merely as a heuristic.
Tue 25th November 2014
Degree distribution of shortest path trees and bias in network sampling algorithms
In this talk, we investigate the degree distribution of shortest path trees of various weighted network models. The aim of many empirical studies is to determine the degree distribution of a network with unknown structure by using trace-route sampling. We derive the limiting degree distribution of the shortest path tree from a single source on various random network models with edge weights: the configuration model and r-regular graphs with i.i.d. power law degrees and i.i.d. edge weights, the complete graph with edge weights that are powers of i.i.d. exponential random variables. We use these results to shed light on an empirically observed bias in network sampling methods.
Tue 18th November 2014
Development of the Bayesian approach in Canadian fisheries stock assessment
Many of the world's harvested fish stocks are managed with the use of results from fish stock assessments. The penultimate goal of fish stock assessment is to evaluate the potential consequences of alternative management options. The bulk of the analytical effort is typically focused on formulating credible models of fish population dynamics, compiling data and fitting the models to data to estimate model parameters and management quantities of interest. Trends in fish stock abundance and fishing mortality rates are evaluated and the models are projected to evaluate the potential consequences of different management options. Since the mid-1990s applications of the Bayesian statistical approach to fish stock assessment have been increasing and in recent years applications have become commonplace. Debates about whether the Bayesian is appropriate for fisheries stock assessment have moved on to debates over how the approach should be applied. In this talk I review recent developments of the Bayesian approach in Canadian fisheries stock assessment with a focus on some of my recent applications to rockfish stocks that have been designated as threatened and endangered. I will highlight some of the chief merits of the approach but also problems commonly experienced with its application.
4192 Earth Sciences Building, 2207 Main Mall
Tue 4th November 2014
Patty Hambler, MEd
In this workshop, Patty Hambler (Acting Associate Director, Strategic Initiatives & Special Projects, Student Development & Services)
- how Early Alert simplifies the process for faculty, TAs, and staff to connect students of concern with campus resources and supports
- the kinds of concerns that are appropriate to submit within Early Alert; and
- how Early Alert protects student privacy and maintains confidentiality.
Early Alert Orientation for Faculty, TAs, and Staff
Supporting student learning and success is a priority for UBC.
Early Alert helps achieve this goal by helping faculty and staff provide more comprehensive support for students who are facing difficulties that put their academic success at risk.
Earlier support to get back on track
With Early Alert, faculty and staff can identify their concerns about students sooner and in a more coordinated way. This gives students the earliest possible connection to the right resources and support, before difficulties become overwhelming.