Tuesday, February 7, 3.15 pm, 2017. Seminar in Statistics.

Correlated Variables in High Dimensional Linear Regression Models: Clustering and Combination of Penalties based Methods
Niharika Gauraha, Indian Statistical Institute, Bangalore and KTH
Abstract: Variable selection in high dimensional regression problems is challenging specially in presence of highly correlated predictors. The Least Absolute Shrinkage and Selection Operator (Lasso) is a widely used regularized regression method for variable selection in high dimensional problems, but it tends to select a single predictor from a group of highly correlated predictors even if many or all of these predictors are relevant. We discuss the following approaches for correlated variable selection:
1. The concept of clustering or grouping correlated predictors and then pursuing group- wise model fitting. For example, cluster Lasso Methods, and Stability Feature Selection using Cluster Representative Lasso etc.
2. Simultaneous clustering and model fitting that involves combination of two different penalties. For example, Elastic Net is a combination of the Lasso penalty (L1) and the Ridge (L2) penalty.
Location: Alan Turing.

Tuesday, February 21, 3.15 pm, 2017. Seminar in Mathematical Statistics.

Non-life (re)insurance pricing: an introduction
Alex Teterukovsky, IF Skadeförsäkring
Abstract: Insurance is about transferring risks between parties. A party who assumes a risk or a portfolio of risks is normally compensated by the other so-called ceding party. We will look into principles for how this compensation is calculated from both sides. The seminar will cover risk-based pricing of single risks, as well as portfolios of risks, both from the perspective of the insured and from that of the insurer. Special attention will be given to reinsurance pricing, i.e. the transfer of risks from the insurer to other insurers. The seminar will focus on practical issues insurance companies face in their daily work.
Take your pens with you, as we'll try to have a hands-on pricing exercise, if time permits.
Location: Hopningspunkten.

Tuesday, March 7, 3.15 pm, 2017. Seminar in Statistics.

A Punctuated Stochastic Model of Adaptation
Krzysztof Bartoszek, Uppsala University and Linköping University
Abstract: Contemporary stochastic evolution models commonly assume gradual change for a phenotype. However the fossil record and biological theory suggests that development is rather undergoing punctuated change. In this setup one assumes that there are very short time intervals during which dramatic change occurs and between these the species are at stasis. Motivated by this I will present a branching Ornstein-Uhlenbeck model with jumps at speciation points. I will in particular discuss a very recent result concerning weak convergence: for a classical Central Limit Theorem to hold dramatic change has to be a rare event.
Location: Alan Turing.

Monday, March 20, 3.15 pm, 2017. Seminar in Mathematical Statistics.

Galton-Watson processes with the expectation kernel having an atom
Serik Sagitov, Mathematical Statistics, Chalmers University
Abstract: Branching processes with infinitely many types are usually studied under the assumptions of the Perron-Frobenius theorem for the expectation kernels. The Perron-Frobenius eigenvalue then gives the growth rate of the process allowing to distinguish between subcritical, critical, and supercritical reproduction regimes. We consider Galton-Watson processes with a general type space whose expectation kernels have a particular structure, ensuring the existence of an embedded single-type branching process. The mean offspring number of the embedded process can be used as a criticality gauge for the multi-type Galton-Watson process even outside the Perron-Frobenius zone. The talk will be given on a level suitable for Ph.D. students.
Location: Hopningspunkten.

Tuesday, April 4, 3.15 pm, 2017. Seminar in Statistics.

Optimal design for dose-finding in clinical trials
Frank Miller, Dept. of Statistics, Stockholm University
Abstract: When new drugs are developed, an important step is to determine the "right" dose to be recommended for patients. If a too low dose would be recommended, the drug would not achieve sufficient effect. If a too high dose would be chosen, patients have increased risk for adverse events. Usually large clinical trials are conducted to determine the right dose. It is important that the design of these so-called dose-finding trials is good to ensure that valuable information is obtained about the relationship between dose and effect of the drug. We apply optimal design theory to determine good designs for dose-finding studies. Since the assumed statistical model for the relationship between dose and effect is a non-linear regression model, optimal designs depend on unknown parameters. We present several methods to deal with this difficulty and illustrate them in a case study.
Location: Alan Turing.

Tuesday, April 18, 3.15 pm, 2017. Seminar in Mathematical Statistics.

On connections between some classical mortality laws and proportional frailty
Mathias Lindholm, Mathematical Statistics, Stockholm University
Abstract: We provide a simple frailty argument that produces the Gompertz-Makeham mortality law as the population hazard rate under the assumption of proportional frailty given a common exponential hazard rate. Further, based on a slight generalisation of the result for the Gompertz-Makeham law the connection to Perks and Beard's mortality laws are discussed. Moreover, we give conditions for which functional forms of the baseline hazard that will yield proper frailty distributions given that we want to retrieve a certain overall population hazard within the proportional frailty framework.
Location: Hopningspunkten.

Tuesday, May 2, 3.15 pm, 2017. Seminar in Statistics.

An overview of measures for population-based cancer survival
Therese Andersson, Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet.
Abstract: I will introduce the field of population-based cancer survival analysis and its role in cancer control. I will especially cover the concept of relative survival and why it is often preferred over cause-specific survival for the study of cancer patient survival using data collected by population-based cancer registers. I will also present different measures of cancer-patient survival such as, the proportion cured, the loss in life expectancy due to cancer and crude vs net probabilities of death. Each of these measures show different aspects of cancer patient survival, and examples from published population-based studies will be presented and discussed.
Location: Alan Turing.

Tuesday, May 16, 3.15 pm, 2017. Seminar in Mathematical Statistics.

Weak convergence of individual Mahalanobis distances
Abstract: Mahalanobis distance (MD) is used in a wide range of applications, such as graphical analysis, outlier detection, discriminant analysis, multivariate calibration, non-normality testing, construction of process control charts and many others. Although the distributional properties of sample MD's are well developed in the case of finite dimension, little is known about the behavior in high-dimensional settings. We present some types of weak convergence of sample Mahalanobis distances, along with some other limiting properties.
Thomas Holgersson, Dept. of Economics and Statistics, Linnæus University
Location: Hopningspunkten.