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Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 16th February 2016
11:00am
Ordering-Free Inference from Locally Dependent Data
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This paper focuses on a situation where data exhibit local dependence but their dependence ordering is not known to the econometrician. Such a situation arises, for example, when many differentiated products are correlated locally and yet the correlation structure is not known, or when there are peer effects among students' actions but precise information about their reference groups or friendship networks is absent. This paper introduces an ordering-free local dependence measure which is invariant to any permutation of the observations, and can be used to express various notions of temporal and spatial weak dependence. The paper begins with the two-sided testing problem of a population mean, and introduces a randomized subsampling approach where one performs inference using U-statistic type (or V-statistic type) test statistics that are constructed from randomized subsamples. The paper shows that one can obtain inference whose validity does not require knowledge of dependence ordering, as long as local dependence is sufficiently "local" in terms of the ordering-free local dependence measure. The method is extended to models defined by moment restrictions. This paper provides results from Monte Carlo studies.

The paper is written with an econometrics journal in mind, but it is fundamentally concerned with a statistics problem.

Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 9th February 2016
11:00am
Nancy Heckman, Statistics Department Head
Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption
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Understanding the energy consumption patterns of different types of consumers is essential in any planning of energy distribution. However, obtaining consumption information for single individuals is often either not possible or too expensive. Therefore, we consider data from aggregations of energy use, that is, from sums of individuals’ energy use, where each individual falls into one of C consumer classes. Although energy companies do have the reported number of individuals in each consumer class, reporting is not always reliable—the exact number of individuals of each class may be unknown. We develop a methodology to estimate the true number of consumers in each class and the expected energy use of each class as a function of time. We apply our method to a data set and study our method via simulation.

This is joint work with Amanda Lenzia, Camila P.E. de Souza, Ronaldo Dias, and Nancy L. Garcia.

BRG
Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 4th February 2016
4:00pm
Dr. Jochen Brumm, Senior Statistical Scientist, Genentech, Inc, Member of the Roche Group, South San Francisco, California
Statistics in Drug Development: A Personal Perspective
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In this talk, I want to give the audience an idea of what the work of a statistician in a pharmaceutical company might look like using my own experiences. I will give two examples of applied statistics during drug development: an innovative strategy for a dose-ranging trial (MCPMod) and the estimation of the probability of success of a phase 3 trial given the mechanism of action of a compound for cardiovascular risk reduction. Emphasis will be on the strategic context of statistical methods within clinical drug development and on desirable skill-sets that help statisticians in the industry to succeed. I will conclude with some general comments that might be helpful for graduate students considering to work in the pharmaceutical industry.


a place of mind, The University of British Columbia

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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