Tuesday, August 28, 3.15 pm, 2018. Seminar in Mathematical Statistics.

Estimation of Kronecker structured covariance based on modified Cholesky decomposition
Chencheng Hao, School of Statistics and Information, Shanghai University of International Business and Economics
Abstract: This paper is to study covariance estimation problems for high dimensional matrix-valued data. We propose a covariance estimator for the matrix-valued data from penalized matrix normal likelihood. Modified Cholesky decomposition of covariance matrix is utilized to construct positive definite estimators. The method is applied for identify parsimony and for producing a statistically efficient estimator of a large covariance matrix of matrix-valued data. Simulation results are illustrated.
Location: Hopningspunkten.

Tuesday, September 25, 3.15 pm, 2018. Seminar in Statistics.

Interval estimation for a binomial proportion
Per Gösta Andersson, Department of Statistics, Stockholm University
Abstract: The construction of a confidence interval for the binomial parameter p is an elementary, yet not trivial problem. Many procedures, using e.g. normal approximation, have been suggested over the years, some of which will be presented and commented upon. We will start by looking at the standard Wald interval and highlight its erratic and generally bad behaviour, before moving on to intervals with substantially improved properties. Priority will be given to accurate coverage probability, although interval length is important to take into consideration.
Location: Alan Turing.

Tuesday, October 9, 3.15 pm, 2018. Seminar in Mathematical Statistics.

Generalized Divide and Color models
Johan Tykesson, Mathematical Sciences, Chalmers University of Technology and University of Gothenburg
Abstract: In this talk, we consider the following model: one starts with a finite or countable set V, a random partition of V and a parameter p in [0,1]. The corresponding Generalized Divide and Color Model is the {0,1}-valued process indexed by V obtained by independently, for each partition element in the random partition chosen, with probability p, assigning all the elements of the partition element the value 1, and with probability 1-p, assigning all the elements of the partition element the value 0.
A very special interesting case of this is the "Divide and Color Model" (which motivates the name we use) introduced and studied by Olle Häggström. A number of quite varied well-studied processes actually fit into this context such as the Ising model, the stationary distributions for the Voter Model and random walk in random scenery.
Some of the questions which we study here are the following. Under what situations can different random partitions give rise to the same color process? What can one say concerning exchangeable random partitions? What is the set of product measures that a color process stochastically dominates? For random partitions which are translation invariant, what ergodic properties do the resulting color processes have? In the talk, we will focus most attention to the case when V is a finite set.
The talk is based on joint work with Jeff Steif.
Location: Hopningspunkten.

Tuesday, October 23, 3.15 pm, 2018. Seminar in Statistics.

A comprehensive approach for predicting pathogenicity in bacteria
Sebastian Sakowski, Faculty of Mathematics and Computer Science, University of Łódź
Abstract: In this presentation, I will discuss a general approach, based on the Binary State Speciation and Extinction (BiSSE) model, for predicting pathogenicity in bacterial populations from microsatellites profiling data. I will in particular discuss an example of using the BiSSE model to estimate parameters from genetic data, exactly from a real dataset of 251 Escherichia coli strains. Additionally, I will briefly review results of a research in the field of DNA computing and molecular programming.
Location: Alan Turing.

Tuesday, November 6, 3.15 pm, 2018. Seminar in Mathematical Statistics.

Fractional limit processes in shot noise models
Ingemar Kaj, Department of Mathematics, Uppsala University
Abstract: A wide variety of random processes and spatial random fields arise naturally as Poisson shot noise models, with shots of random location and size. Such models with power-law size intensities, display a range of limit processes under aggregation and suitable scaling of parameters. We discuss the various scaling regimes and their limits, which include fractional Brownian motion, fractional Poisson type motions, and stable processes, Allowing for a type of dependence between shots, yet another hybrid Gaussian-Poisson model appears in the limit.
Location: Hopningspunkten.

Tuesday, December 18, 3.15 pm, 2018. Seminar in Statistics.

Complier average causal effect analysis using growth mixture modeling
Hugo Hesser, Department of Behavioural Sciences and Learning, Linköping University
Abstract: Randomized controlled trials (RCTs) offer a unique opportunity to test causal effects. However, treatment noncompliance is a common problem in RCTs and is a major threat to causal inferences. Intention-to-treat analysis (ITT), which is widely used to estimate treatment effects in RCTs, does not provide an estimate of the effect of treatment per se in the presence of noncompliance, and ad hoc methods for dealing with noncompliance in RCTs (e.g. as-treated or per-protocol analysis) do not provide an unbiased estimate of the average causal effect (ACE). The current presentation will focus on how an unbiased local estimate of (L)ACE can be obtained for the subgroup of compliers in RCTs using complier average causal effects (CACE) analysis. The talk will focus on model identification, specifications and assumptions for obtaining maximum likelihood estimates in CACE models using growth mixture modeling in a structural equation modeling framework. Priority will be given to CACE modeling in practice for evaluating non-pharmacological treatments, and CACE analysis will be illustrated with a randomized controlled add-on component trial of an internet-delivered psychological treatment for irritable bowel syndrome (Hesser et al., 2017, Psychological Medicine).
Location: Alan Turing.