Statistics Colloquium

### Speaker: Okan Bulut

Title: Profile Analysis of Multivariate Data Using the profileR Package

Location: Room 5, Háskólabíó.

Time: Wednedsay, October 28, at 11:00-12:00.

### Abstract:

Profile analysis is a psychometric clustering technique that is the equivalent of a repeated measures extension of the multivariate analysis of variance model. Profile analysis is used by researchers and practitioners to identify whether two or more groups of individuals have significantly distinct or similar profiles based on a set of continuous variables (e.g., test scores on a battery of tests). Profile analysis involves the quantification of the elevation, variation, and parallelism of multiple variables across groups. The profileR package (Bulut & Desjardins, 2015) in R can perform several profile analytic methods, including criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, profile analysis by group, and a within-person factor model to derive score profiles. This presentation will provide a brief introduction about common profile analytic techniques and demonstrate their application using the profileR package in R.

Statistics Colloquium

### Speaker: Daníel F. Guðbjartsson, Department of Statistics, deCODE genetics

Title: Estimating the effect of a sequence variant on correlated phenotypes

Location: Lögberg, 201

Time: Friday October 3 at 12:00-13:00.

### Abstract:

Some variations in the human genome associate with multiple correlated phenotypes. This leads naturally to questions about conditional independence. E.g.: Given the association between a sequence variant and a phenotype, is the association between the variant and a second phenotype significant? It is relatively easy to create statistical tests for conditional independence but concluding about biological mechanisms from

the results of these tests must be done with great care. This is demonstrated through several important examples.

Statistics colloquium

### Speaker: Douglas P. Wiens, Department of Mathematical and Statistical Sciences, University of Alberta

Title: Robustness of Design: A Survey

Location: V-147, VRII

Time: Friday September 12, at 12:00-13:00

### Abstract:

When an experiment is conducted for purposes which include fitting a particular model to the data, then the ‘optimal’ experimental design is highly dependent upon the model assumptions – linearity of the response function, independence and homoscedasticity of the errors, etc. When these assumptions are violated the design can be far from optimal, and so a more robust approach is called for. We should seek a design which behaves reasonably well over a large class of plausible models.

I will review the progress which has been made on such problems, in a variety of experimental and modelling scenarios – prediction, extrapolation, discrimination, survey sampling, dose-response, etc.

The week April 22–25, 2014 will be a statistics week. Over these days there will be four statistics events.

All the talks will be in room V-157 in VR-II on the UI campus.

- April 22 at 14:00.
**Emtiyaz Khan** from École Polytechnique Fédérale de Lausanne: *Machine Learning*
- April 23 at 12:00.
**R-Iceland**: *Opening meeting of R-Iceland, a community of R-users in Iceland*
- April 23 at 13:00.
**Andrew Gelman** from Columbia University: *The statistics package Stan*
- April 25 at 12:00.
**Håvard Rue** from Norwegian University of Science and Technology: *Penalising model component complexity: A principled practical approach to constructing priors*

Continue reading 'Statistics Week April 22-25'»

Statistics Colloquium

### Speaker: Philippe Crochet, Research Specialist at the Icelandic Meteorological Office

Title: Estimating the flood frequency distribution at ungauged catchments using a regional flood frequency analysis

Location: V-157, VRII

Time: Thursday, April 10th, 12:00-13:00.

### Abstract:

Extreme events such as floods may have serious societal and environmental consequences. It is therefore necessary to develop adequate models for the estimation of such events. Often, this information is required at sites where measured streamflow series are either too short to allow a robust estimation of extreme flood quantiles, or where no data is available at all. We explore the possibility of conducting a regional flood frequency analysis (RFFA) to reliably predict flood quantiles at both gauged and ungauged sites. The principle of the method is to transfer information from gauged sites to a target site (gauged or ungauged) within a homogeneous region. The principle of the method is first presented and then tested in Northern Iceland, using available streamflow observations and then streamflow simulations from a distributed hydrological model.

Statistics colloquium talk

### Speaker: Guðmundur Einarsson, M.S. student of Statistics, University of Iceland

### Title: Discussion on evolutionary algorithms in optimization

Location: Room V-157 in building VR-II on the UI campus

Time: Thursday, April 3rd 2014, at 12:00 to 13:00.

#### Abstract:

Two papers will be discussed. First, a paper entitled “Metaheuristics-the metaphor exposed” by K. Sörensen. Summary of paper:

In recent years, the field of combinatorial optimization has witnessed a true tsunami of “novel” metaheuristic methods, most of them based on a metaphor of some natural or man-made process. The behavior of virtually any species of insects, the flow of water, musicians playing together — it seems that no idea is too far-fetched to serve as inspiration to launch yet another metaheuristic. In this paper we will argue that this line of research is threatening to lead the area of metaheuristics away from scientific rigour. We will examine the historical context that gave rise to the increasing use of metaphors as inspiration and justification for the development of new methods, discuss the reasons for the vulnerability of the metaheuristics field to this line of research, and point out its fallacies. At the same time, truly innovative research of high quality is being performed as well. We conclude the paper by discussing some of the properties of this research and by pointing out some of the most promising research avenues for the field of metaheuristics.

Secondly, a paper entitled “On the Hunt: Competitive Coevolution as a Metaheuristic” by Guðmundur Einarsson, Tómas Philip Rúnarsson and Gunnar Stefánsson. Summary of paper:

A predator prey model for competitive coevolution is used as a metaheuristic. Two scenarios are created. In each scenario the predator genetic setup consists of parameters for optimization procedures. The prey genetic setup consists of starting points for the optimization of a specific function. A sampling method is explored for the relative fitness assessment where the number of fitness evaluations can be controlled similar to K-fold relative fitness assessment. The historical evolution of the prey is explored as a diagnostics tool for multimodality.

Málstofa í tölfræði

### Speaker: Óli Páll Geirsson, Doctoral student, Department of Mathematics, Faculty of Physical Sciences, University of Iceland.

### Title: Modelling annual maximum 24 hour precipitation in Iceland using the SPDE approach and split sampling MCMC strategy

Location: Room V-157 in building VR-II on the UI campus

Time: Thursday, March 20th 2014, at 12:00 to 13:00.

Abstract: To obtain predictions of distributional properties of extreme precipitation on a high resolution grid both observations and outputs from climate models are relied upon. A method for quantile predictions of extreme precipitation which combines these two sources is developed. The method is based on observations of annual maximum 24 hour precipitation from 86 observation sites in Iceland and outputs from a climate model on a 1 km by 1 km regular grid.

A covariate based on the climate model which represents the intensity of precipitation can be computed at each grid point. A latent Gaussian model is built for the observations with the intensity measure as a covariate which in turn allows for spatial predictions on the high resolution grid. The observations are assumed to follow the generalized extreme value distribution. In order to make fast continuous spatial predictions, SPDE spatial models are implemented at the latent level of the model for both the location and scale parameters of the proposed data distribution.

A novel MCMC strategy called split sampling, is implemented for model inference. The strategy greatly improves the mixing and convergence properties of the MCMC sampler. Inferring the parameters of the model yields posterior estimates for the distribution of maximum annual 24 hour precipitation in every grid point. In particular, a quantile estimate can be given at every grid point.

Málstofa í tölfræði

### Speaker: Daniel Simpson, Researcher, Department of Mathematical Sciences, Norwegian University of Science and Technology

### Title: Towards computationally efficient spatial statistics

Location: Room V-157 in building VR-II on the UI campus

Time: Thursday January 23rd 2014, at 12:00 to 13:00.

Abstract: Spatial statistics is hard! The combination of large datasets and complicated models leads to a large number of computational and modelling challenges. In this talk, I will outline a framework for reducing this complexity through a combination of approximate models and approximate inference.