Diana Cole’s book Parameter Redundancy and Identifiability has been publish by Chapman and Hall/CRC

Book Synopsis

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context. Key features of this book:

• Detailed discussion of the problems caused by parameter redundancy and non-identifiability
• Explanation of the different general methods for detecting parameter redundancy and non-identifiability, including symbolic algebra and numerical methods
• Chapter on Bayesian identifiability
• Throughout illustrative examples are used to clearly demonstrate each problem and method. Maple and R code are available for these examples
• More in-depth focus on the areas of discrete and continuous state-space models and ecological statistics, including methods that have been specifically developed for each of these areas

This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.

Book website: https://www.routledge.com/Parameter-Redundancy-and-Identifiability/Cole/p/book/9781498720878

Code for book available at: https://www.kent.ac.uk/smsas/personal/djc24/parameterredundancy.html

Congratulations to Rachel for her well-deserved promotion to professor, congratulations Professor McCrea!

SE@K had a meal at Cafe Des Amis in Canterbury to celebrate recent successes of the group:

Rachel being awarded Guy Bronze Medal ; Byron being awarded the Barnett award ; Eleni being awarded £303 200 by NERC for the project “Integrating new statistical frameworks into eDNA survey and analysis at the landscape scale”

Rachel has been awarded the 2020 Guy Bronze Medal by the Royal Statistical Society (RSS).

The Guy Medal in Bronze has been awarded for her innovative and novel work in statistical ecology, with particular reference to the development of goodness-of-fit tests and model selection strategies for complex ecological data. Important areas include (multi-state) capture-recapture-type models and integrated models. Notable publications include: the 2017 JRSSC paper ‘A new strategy for diagnostic model assessment in capture-recapture’, which identified a direct relationship between particular diagnostic tests and score tests; and the 2020 JRSSC paper ‘Diagnosing heterogeneity in transition probabilities in multistate capture-recapture data’, which developed new tests to identify unmodelled transition heterogeneity.

Professor Deborah Ashby, President of the Royal Statistical Society, said: “Dr McCrea has made a profound contribution to statistical ecology. The Society’s journals have published a number of noteworthy papers authored by Rachel, and her development of goodness-of-fit tests and model selection strategies has been particularly innovative.”

The medal will be presented to Rachel at the RSS Annual Conference in Bournemouth in September.

Emeritus Professor of Statistics, Byron Morgan, has been awarded the Barnett Award by the Royal Statistical Society.

Professor Deborah Ashby, President of the Royal Statistical Society, said: “Professor Morgan has had a great influence on the world of statistics and statistical ecology. His innovative work on computationally efficient methods for co-analysis of the UK Butterfly Monitoring Scheme has led to great insights into biodiversity and he had been a significant figure in creating better networks of statistical ecologists.”

Byron will be presented with the award at the Royal Statistical Society Annual Conference in September 2020 where he will also give a keynote presentation.

New publication in JABES: Statistical Development of Animal Density Estimation Using Random Encounter Modelling by Natoya Jourdain, Diana Cole, Martin Ridout and Marcus Rowcliffe.

Abstract:

Camera trapping is widely used in ecological studies to estimate animal density, although these studies are largely restricted to animals that can be identified to the individual level. The random encounter model, developed by Rowcliffe et al. (J Anal Ecol 45(4):1228–1236, 2008), estimates animal density from camera-trap data without the need to identify animals. Although the REM can provide reliable density estimates, it lacks the potential to account for the multiple sources of variance in the modelling process. The density estimator in REM is a ratio, and since the variance of a ratio estimator is intractable, we examine and compare the finite sample performance of many approaches for obtaining confidence intervals via simulation studies. We also propose an integrated random encounter model as a parametric alternative, which is flexible and can incorporate covariates and random effects. A data example from Whipsnade Wild Animal Park, Bedfordshire, south England, is used to demonstrate the application of these methods.

The paper Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference, first published on the the 2nd of May 2018, was among the top cited articles in recent publication history for Ecological Monographs.