Statistics seminar


Friday March 23rd at 12.15-14 in MaD380

Speaker: Anton Muravev

Title: Metaheuristics and Evolutionary Algorithms: The Overview

Abstract: Metaheuristics are general-purpose heuristic optimization algorithms that do not use any information about the problem, requiring only the evaluation of candidate solutions. In addition to solving black-box problems, their properties may be desirable when the domain knowledge is not easily applicable or the fitness landscape is too complex. In particular, evolutionary algorithms (EA) are some of the most widespread metaheuristics with numerous practical applications. We aim to provide a general overview of the field, its most essential concepts and achievements along with some practical considerations.

In this seminar we will consider some historical aspects of metaheuristic optimization, the origins of evolutionary computation, its fundamental advantages and limitations. We describe the terminology and the general framework of the EA design, as well as some commonly used operators and techniques. We then briefly cover the multitude of the most relevant variants of evolutionary algorithms and outline their respective application areas. Finally, we consider the problem of neuroevolution – the use of evolutionary algorithms to optimize the architecture and/or weights of the problem-specific neural network. As human-designed neural architectures are approaching their limits, the neuroevolution research is experiencing a newfound growth; we thus explore some of the current developments in this area.


Wednesday February 28th at 13:00-14.00 in MaA210

Speaker: Essi Syrjälä

Title: Joint modeling approaches of food consumption and the risk of islet autoimmunity (pre-T1D)

Abstract: Pre-T1D is a preclinical phase that is identified by the presence of type 1 diabetes (T1D) -associated autoantibodies. Some evidence on the association between the early nutrition and the development of pre-T1D or T1D exists but no specific dietary factor has yet been shown to be an unambiguous risk factor.

A prospective birth cohort of 6069 infants born in 1996-2004 with genetic susceptibility to T1D was recruited. Child’s diet was measured with 3-day food records at the ages of 3, 6, 12, 24, 36, 48, 60 and 72 months and T1D-associated autoantibodies were measured at 3 to 12-month intervals up to the age of 15 years.

 We used a time-dependent Cox model, a basic joint model and a joint latent class mixed model to investigate the association between food consumption and pre-T1D, separately. Whereas a time-dependent Cox is a single model, joint models couple a survival model with a linear mixed effects model, which enables the modeling of two phenomena at the same time efficiently. Joint models have great potential in nutritional epidemiological studies based on (i) their ability to identify the individual exposure trajectories even when information is observed only at some measuring points that can themselves include missing values, (ii) their ability to reduce the measurement error common with nutritional data and (iii) the ability of joint latent class mixed models to potentially detect periods of sensitivity and risk groups. We found that different models revealed different features of the nutritional data and our findings regarding that will be presented.


Friday February 9th at 12:15-14 in MaD381

Speaker: Gleb Tikhonov (University of Helsinki)

Title: Analysis of ecological community data with latent factor models

Abstract: Last decade has brought significant expansion to the methodological tools that are available for an ecologist interested in analysis of data on ecological communities. Instead of previously commonly used ordination techniques, a new branch of model-based statistical methods has emerged, which is called joint species distribution models (JSDM). While different JSDMs has been constructed based on very different machine learning techniques, a particularly big group of powerful and flexible models is designed upon latent factors approach. In my talk I will present our ongoing development on such latent factor-based JSDM, which is called a Hierarchical Model of Species Communities (HMSC). While in its most simple version, HMSC is just a combination of generalized linear mixed model with sparse Bayesian latent factor model, we have implemented a set of important extensions that are much desired in practical analysis of ecological data. Thus, our framework is capable to account for the additional data on species traits and phylogenic relationships, deal with hierarchical and spatially explicit sampling designs, account for potential non-stationarity in species associations, and finally be efficiently used in time-series analysis.


Friday January 26th at 10:15-12 in MaD 355.

Speaker: Sara Taskinen (University of Jyväskylä)

Title: Blind source separation based on robust autocovariance matrices

Abstract: Assume a Blind Source Separation (BSS) model, that is, the observed p time series are assumed to be linear combinations of p latent uncorrelated weakly stationary time series. The aim is then to find an estimate for the unmixing matrix which transforms the observed time series back to uncorrelated latent time series. In the classical SOBI (Second Order Blind Identification) method, approximate joint diagonalization of the sample covariance matrix and sample autocovariance matrices with several lags is used to estimate the unmixing matrix. However, it is well known that in the presence of outliers, the sample covariance matrix and sample autocovariance matrices perform poorly and yield to unreliable unmixing matrix estimates. In this talk we thus propose a robust SOBI method which uses so-called M-autocovariance matrices in the estimation. We use finite-sample simulation studies and a real data example to illustrate the performance of our method.