Tuesday, March 5, 3.15 pm, 2019. Seminar in Mathematical Statistics.

Muller's Ratchet in Populations Doomed to Extinction
Peter Olofsson, Department of Mathematics, Physics and Chemical Engineering, Jönköping University
Abstract: Muller's ratchet is the process by which asexual populations accumulate deleterious mutations in an irreversible manner. Most mathematical models have been of the Wright-Fisher type with fixed population size and relative fitness. In contrast, we use a branching process model with absolute fitness, leading to unavoidable extinction. Individuals are divided into classes depending on how many mutations they have accumulated, and we give results for the rate of the ratchet and the size of the fittest class.
Location: Hopningspunkten.

Tuesday, April 16, 3.15 pm, 2019. Seminar in Statistics.

Single cell analysis and cell type identification in medical research
Sandra Lilja, Division of Children's and Women's Health, Department of Clinical and Experimental Medicine, Linköping University
Abstract: Many patients today do not respond to treatment and an important reason for this may be the involvement of thousands of genes in multiple cell types. Single cell analysis can thus help to gain a systems level understanding of diseases, in order to find efficient diagnostic markers and treatments, as it allows for analysis of the expression of all genes in thousands of individual cells, one by one.
A bottleneck during single cell analysis is the computational identification of the different cell types in the tissue. During the laboratory procedure, the transcriptome of the different cells is separated and marked before sequencing. The transcript count for each gene in each cell can thus be identified. Using the transcriptomic profile of all the different cells in the samples, they can then be clustered into groups, and the corresponding cell type for each cluster identified. There are many different methods and statistical packages available for clustering and cell type identification, using some different approaches. These work well in different situations, though choosing the best approach is not always easy. During the seminar I will present the most commonly used methods today, how, and when they are used. I will also go through potential drawbacks, and when cell typing using these techniques may be problematic or fail.
Location: Alan Turing.

Tuesday, May 7, 3.15 pm, 2019. Seminar in Mathematical Statistics.

Statistical Learning as a Compression Problem from the Information Theory Perspective
Chun-Biu Li, Mathematical Statistics, Department of Mathematics, Stockholm University
Abstract: Although it was introduced in the context of communication theory, modern information theory provides us with a nonparametric probabilistic framework for statistical learning free from a priori assumption on the underlying statistical model. In this talk, I will discuss some of the information theory based methods for unsupervised and supervised learning. In particular, the soft (fuzzy) clustering problem in unsupervised learning can be viewed as a tradeoff between data compression and minimizing the distortion of the data. Similarly, modeling in supervised learning can be treated as a tradeoff between compression of the predictor variables and retaining the relevant information about the response variable. To illustrate the usage of these methods, some applications in biophysical problems and time series analysis will be addressed in the talk.
Location: Hopningspunkten.

Tuesday, May 14, 3.15 pm, 2019. Seminar in Statistics.

Usage of gaps between observations
Magnus Ekström, Statistics, Umeå School of Business, Economics and Statistics, Umeå University
Abstract: In this talk, I will provide a brief overview of some basic ideas in the theory of gaps between successive observations (a.k.a. spacings or sample spacings). After reviewing some basic properties, I will discuss the use of spacings in estimating parameters and in testing statistical hypotheses. It will be argued that such methods of statistical inference have asymptotic properties that closely parallel those of likelihood-based methods in regular parametric models. Moreover, they can be shown to work also in unbounded likelihood problems, where both the maximum likelihood method and the generalized likelihood ratio test may break down. Unlike the maximum likelihood estimators, some variants of the estimators based on spacings are quite robust under heavy contamination.
Location: Alan Turing.