Category Archives: Statistics

Statistics job resources

Applying for an academic job is serious work.  I ended up lucky (though, luck favors the prepared (Louis Pasteur)).  I received two job offers this season and took my first-choice job.  But I worked hard to get those offers.  I kept a detailed CV my entire student career (starting as a BA student, not waiting until job season to start), wrote an extensive teaching dossier for the 20 courses I’ve taught and ugrad tutoring experience, and developed a research statement as that vision became clearer to me.  Clearly, self-investment and personal excellence are the most important ingredients.  Next is to find people who want to hire you.
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tdllicor: estimates discrimination and other parameters associated with leaf photosynthesis

Together with David Hanson, I developed R package tdllicor which reads TDL and Licor files, aligns them, and calculates quantities of interest with bootstrap intervals.  It is currently private as it is specialized and not of general interest.  It has already been important for a number of conference publications and is used for active research:
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mortest: estimates the total number of carcasses at a windfarm

Working with Aaftab Jain, we developed a estimator for total number of bird and bat carcasses at a windfarm called “mortest” and implemented it as an R package.  We are interested in estimating c, the total number of carcasses (mortalities) in a period (year). The total number of carcasses is the sum of carcasses over size classes, c = sum_s=1^S c_s. If carcasses are retained (that is, not scavenged) and searcher efficiency is perfect (every carcass is found) and every tower is searched, then each c_s would be counted perfectly. Yet, carcass scavenging by predators and searchers overlooking carcasses are a reality, making observed counts an underestimate. Furthermore, tower sampling rather than censusing is a cost-saving convenience. Our estimator of total mortality, c, weighs the estimates from different search intervals and adjusts the observed counts for scavenging, search efficiency, searchable area of each tower, and proportion of towers searched, accounting for uncertainty in these estimates using a bootstrap.
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Talk: ACASA Annual meeting 2011

I’ll be giving a shortened version of my Bayesian stable isotope mixing model talk (title and abstract below) at the Albuquerque Chapter of the American Statistical Association (ACASA) annual meeting on Friday, April 29, 2011. I gave two distinct longer versions of this talk recently as part of job interview talks at St. Louis University and the University of New Mexico.  I’m looking forward to the meeting to visit with people who I’ve worked with over the last several years, organizing judging events at science fairs, and other events.
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Wishart distribution in WinBUGS, nonstandard parameterization

The Wishart distribution and especially the inverse-Wishart distribution are the source of some confusion because they occasionally appear with alternative parameterizations. Also, the Wishart distribution can be used to model a covariance matrix or a precision matrix (the inverse of a covariance matrix) in different situations, and the inverse-Wishart the same, but the other way round. It’s already becoming complicated. Hal Stern, coauthor of Bayesian Data Analysis (BDA), helped to clarify many issues for me in an email conversation. In this post I hope to clarify the differences in Wishart parameterizations of BDA, the wikipedia pages, and the WinBUGS and OpenBUGS softwares, and show an example in OpenBUGS where the inverse parameterization has to be specified relative to the distribution’s definition for the correct posterior to result.
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