Sign in with your institution or organization account (Single Sign-on): see "News"

14-18 August 2017

Uppsala University

Europe/Stockholm timezone

- contact@eysm2017.eu

### Contact

Home > Contribution List

## Talks

Displaying 38
contributions
out of
38

Type: Speaker

Post-selection inference has been considered a crucial topic in data
analysis. In this article, we develop a new method to obtain correct inference after model selection by the Akaike's information criterion Akaike (1973) in linear regression models. Confidence intervals can be calculated by incorporating the randomness of the model selection in the distribution of the parameter estimators which
... More

Presented by Mr. Ali CHARKHI
on
14 Aug 2017
at
16:00

Type: Speaker

The article addresses the best unbiased estimators of the block compound symmetric covariance
structure for m-variate observations with equal mean vector over each level of factor or each time point (model with structured mean vector). Under multivariate normality, the free-coordinate approach is used to obtain unbiased linear and quadratic estimates for the model parameters. Optimality of these
... More

Presented by Mr. Arkadiusz KOZIOŁ
on
15 Aug 2017
at
16:30

Type: Speaker

Biomonitoring of waterbodies is vital as the number of anthropogenic stressors on aquatic ecosystems keeps growing. However, the continuous decrease in funding makes it impossible to meet monitoring goals or sustain traditional manual sample processing. We review what kind of statistical tools can be used to enhance the cost efficiency of biomonitoring: We explore automated identification of fresh
... More

Presented by Dr. Johanna ÄRJE
on
14 Aug 2017
at
14:00

Type: Speaker

Cox proportional hazards model with measurement errors in covariates is considered. It is the ubiquitous technique in biomedical data analysis. In Kukush et al. (2011) [ Journal of Statistical Research **45**, 77-94 ] and Chimisov and Kukush (2014) [ Modern Stochastics: Theory and Applications **1**, 13-32 ] asymptotic properties of a simultaneous estimator $(\lambda_n;\beta_n)$ for the basel
... More

Presented by Ms. Oksana CHERNOVA
on
17 Aug 2017
at
16:00

Type: Speaker

Branching processes are relevant models in the development of theoretical approaches to problems in applied fields such as, for instance, growth and extinction of populations, biology, epidemiology, cell proliferation kinetics, genetics and algorithm and data structures. The most basic model, the so-called Bienaymé-Galton-Watson process, consists of individuals that reproduce independently of the
... More

Presented by Ms. Carmen MINUESA ABRIL
on
15 Aug 2017
at
14:00

Type: Speaker

In this paper we study the problem of modelling the integer-valued vector observations. We consider the BINAR(1) models defined via copula-joint innovations. We review different parameter estimation methods and analyse estimation methods of the copula dependence parameter. We also examine the case where seasonality is present in integer-valued data and suggest a method of deseasonalizing them. Fin
... More

Presented by Andrius BUTEIKIS
on
18 Aug 2017
at
11:00

Type: Speaker

This paper addresses a problem of linear and logistic model selection in the presence of both continuous and categorical predictors. In the literature two types of algorithms dealing with this problem can be found. The first one well known group lasso (\cite{group}) selects a subset of continuous and a subset of categorical predictors. Hence, it either deletes or not an entire factor. The second
... More

Presented by Dr. Agnieszka PROCHENKA
on
15 Aug 2017
at
10:30

Type: Speaker

We study $E$-optimal block designs for comparing a set of test treatments with a control treatment. We provide the complete class of all $E$-optimal approximate block designs and we show that these designs are characterized by simple linear constraints. Employing the provided characterization, we obtain a class of $E$-optimal exact block designs with unequal block sizes for comparing test treatmen
... More

Presented by Mr. Samuel ROSA
on
17 Aug 2017
at
14:00

Type: Speaker

This talk concerns estimation of the diffusion parameter of a diffusion process observed over a fixed time interval. We present conditions on approximate martingale estimating functions under which estimators are consistent, rate optimal, and efficient under high frequency (in-fill) asymptotics. Here, limit distributions of the estimators are non-standard in the sense that they are generally norma
... More

Presented by Nina Munkholt JAKOBSEN
on
16 Aug 2017
at
14:00

Type: Speaker

In the following we deal with estimates for distributions of Hölder semi-norms of sample functions of random processes from spaces $\mathbb{F}_\psi(\Omega)$, defined on a compact metric space and on an infinite interval $[0,\infty)$, i.e. probabilities
$$\mathsf{P}\left\{\sup\limits_{\substack{0<\rho(t,s)\le\varepsilon \\ t,s\in\mathbb{T}}} \frac{|X(t)-X(s)|}{f(\rho(t,s))}>x\right\}.$$
Such est
... More

Presented by Mr. Dmytro ZATULA
on
16 Aug 2017
at
14:30

Type: Speaker

Recently, there has been an increasing interest on the combination of copulas with a finite mixture model. Such a framework is useful to reveal the hidden dependence patterns observed for random variables flexibly in terms of statistical modeling. The combination of vine copulas incorporated into a finite mixture model is also beneficial for capturing hidden structures on a multivariate data set.
... More

Presented by O. Ozan EVKAYA
on
17 Aug 2017
at
09:00

Type: Speaker

We consider three interlinked problems in stochastic geometry: (1) constructing optimal multicouplings of random vectors; (2) determining the Fréchet mean of probability measures in Wasserstein space; and (3) registering collections of randomly deformed spatial point processes. We demonstrate how these problems are canonically interpreted through the prism of the theory of optimal transportati
... More

Presented by Dr. Yoav ZEMEL
on
15 Aug 2017
at
15:30

Type: Speaker

In the modern era of high and infinite dimensional data, classical statistical
methodology is often rendered inefficient and ineffective when confronted
with such big data problems as arise in genomics, medical imaging, speech
analysis, and many other areas of research. Many problems manifest when
the practitioner is required to take into account the covariance structure
of the data during hi
... More

Presented by Adam KASHLAK
on
16 Aug 2017
at
09:00

Type: Speaker

In contrast to the low dimensional case, variable selection under the
assumption of sparsity in high dimensional models is strongly influenced by the
effects of false positives.
The effects of false positives are tempered by combining the variable selection
with a shrinkage estimator, such as in the lasso, where the selection is
realized by minimizing the sum of squared residuals regularized
... More

Presented by Mr. Bastien MARQUIS
on
17 Aug 2017
at
14:30

Type: Invited Speaker

Presented by Jane HILLSTON
on
16 Aug 2017
at
13:00

Type: Invited Speaker

In independent component analysis it is assumed that the observed random variables are linear combinations of latent, mutually independent random variables called the independent components. It is then often thought that only the non-Gaussian independent components are of interest and the Gaussian components simply present noise. The idea is then to make inference on the unknown number of non-Gaus
... More

Presented by Hannu OJA
on
17 Aug 2017
at
13:00

Type: Invited Speaker

In independent component analysis it is assumed that the observed random variables are linear combinations of latent, mutually independent random variables called the independent components. It is then often thought that only the non-Gaussian independent components are of interest and the Gaussian components simply present noise. The idea is then to make inference on the unknown number of non-Gaus
... More

Presented by Hannu OJA
on
17 Aug 2017
at
11:00

Type: Invited Speaker

Many questions concerning environmental risk can be phrased as spatial extreme value problems. Classical extreme value theory provides limiting models for maxima or threshold exceedances of a wide class of underlying spatial processes. These models can then be fitted to suitably defined extremes of spatial datasets and used, for example, to estimate the probability of events more extreme than we h
... More

Presented by Jenny WADSWORTH
on
15 Aug 2017
at
09:00

Type: Invited Speaker

Presented by Svante JANSON
on
18 Aug 2017
at
09:00

Type: Invited Speaker

Presented by Jane HILLSTON
on
16 Aug 2017
at
11:00

Type: Invited Speaker

Sequential Monte Carlo methods form a class of genetic-type algorithms sampling, on-the-fly and in a very general context, sequences of probability measures. Today these methods constitute a standard device in the statistician's tool box and are successfully
applied within a wide range of scientific and engineering disciplines. This talk is split into two parts, where the first provides an intro
... More

Presented by Jimmy OLSSON
on
14 Aug 2017
at
13:00

Type: Invited Speaker

Sequential Monte Carlo methods form a class of genetic-type algorithms sampling, on-the-fly and in a very general context, sequences of probability measures. Today these methods constitute a standard device in the statistician's tool box and are successfully
applied within a wide range of scientific and engineering disciplines. This talk is split into two parts, where the first provides an intro
... More

Presented by Jimmy OLSSON
on
14 Aug 2017
at
11:00

Type: Speaker

We propose a Bayesian nonparametric mixture model for the joint full reconstruction of $m$ dynamical equations,
given $m$ observed dynamically-noisy-corrupted chaotic time series. The method of reconstruction is based on the Pairwise Dependent Geometric Stick Breaking Processes mixture priors (PDGSBP) first proposed by Hatjispyros et al. (2017). We assume that
each set of dynamical equations ha
... More

Presented by Mr. Christos MERKATAS
on
17 Aug 2017
at
09:30

Type: Speaker

In this paper the Mallows' model based on Lee distance is considered and compared to models induced by other metrics on the permutation group. As an illustration, the complete rankings from the American Psychological Association election data are analyzed.

Presented by Mr. Nikolay NIKOLOV
on
17 Aug 2017
at
15:30

Type: Speaker

Independent component analysis (ICA) is a popular means of dimension reduction for vector-valued random variables. In this short note we review its extension to arbitrary tensor-valued random variables by considering the special case of two dimensions where the tensors are simply matrices.

Presented by Mr. Joni VIRTA
on
14 Aug 2017
at
15:30

Type: Speaker

This contribution is focused on the kernel conditional density estimations (KCDE). The estimation depends on the smoothing parameters which influence the final density estimation significantly. This is the reason why a requirement of any data-driven method is needed for bandwidth estimation. In this contribution, the cross-validation method, the iterative method and the maximum likelihood approach
... More

Presented by Ms. Katerina KONECNA
on
15 Aug 2017
at
13:30

Type: Speaker

We present a model for multivariate functional data that simultaneously model vertical and horisontal variation.
Horisontal variation is modeled using warping functions represented by latent gaussian variables.
Vertical variation is modeled using Gaussian processes using a generally applicable low-parametric covariance structure.
We devise a method for maximum likelihood estimation using a La
... More

Presented by Mr. Niels OLSEN
on
15 Aug 2017
at
16:00

Type: Speaker

Functional principal component analysis (FPCA) is the key technique for dimensionality reduction and detection of main directions of variability present in functional data. However, it is not the most suitable tool for the situation when analyzed dataset contains repeated or multiple observations, because information about repeatability of measurements is not taken into account. Multilevel functi
... More

Presented by Zuzana ROŠŤÁKOVÁ
on
14 Aug 2017
at
16:30

Type: Speaker

In applications the properties of a stochastic feature often change gradually rather than
abruptly, that is: after a constant phase for some time they slowly start to vary. The goal of this talk is to introduce an estimator for the location of a gradual change point in the jump characteristic of a
discretely observed Ito semimartingale. To this end we propose a measure of time variation for the
... More

Presented by Mr. Michael HOFFMANN
on
14 Aug 2017
at
14:30

Type: Speaker

The Markov-modulated infinite-server queue is a queueing system with infinitely many servers, where the arrivals follow a Markov-modulated Poisson process (MMPP), i.e. a Poisson process with rate modulating between several values. The modulation is driven by an underlying and unobserved continuous time Markov chain $\{X_t\}_{t\geq 0}$. The inhomogeneous rate of the Poisson process, $\lambda(t)$, s
... More

Presented by Ms. Birgit SOLLIE
on
15 Aug 2017
at
14:30

Type: Speaker

Influenza viruses are responsible for annual epidemics, causing more than 500,000 deaths per year worldwide. A crucial question for resource planning in public health is to predict the morbidity burden of extreme epidemics. We say that an epidemic is extreme whenever the influenza incidence rate exceeds a high threshold for at least one week. Our objective is to predict whether an extreme epidemic
... More

Presented by Maud THOMAS
on
16 Aug 2017
at
09:30

Type: Speaker

The branching process theory is widely used to describe a population dynamics in which particles live and produce other particles through their life, according to given stochastic birth and death laws. The theory of General Branching Processes (GBP) presents a continuous time model in which every woman has random life length and gives birth to children in random intervals of time. The flexibility
... More

Presented by Dr. Plamen TRAYANOV
on
18 Aug 2017
at
11:30

Type: Speaker

In this paper some recent advances in goodness of fit testing are presented. Special attention is given to goodness of fit tests based on equidistribution and independence characterizations. New concepts are described through some modern exponentiality tests. Their natural generalizations are also proposed. All tests are compared in Bahadur sense.

Presented by Dr. Bojana MILOŠEVIĆ
on
15 Aug 2017
at
13:00

Type: Speaker

In the case of traditional Ensemble Kalman Filter (EnKF), it is known that the filter error does not
grow faster than exponentially for a fixed ensemble size. The question posted in this contribution is whether the upper bound for the filter error can be improved by using an improved covariance estimator that comes from the right parameter subspace and has smaller asymptotic variance. Its effect
... More

Presented by Marie TURČIČOVÁ
on
17 Aug 2017
at
16:30

Type: Speaker

We introduce the notions of multivariate auto-distance covariance and correlation functions
for time series analysis. These concepts have been recently discussed in the context
of both independent and dependent data but we extend them in a different direction by
putting forward their matrix version. Their matrix version allows us to identify possible
interrelationships among the components of
... More

Presented by Dr. Maria PITSILLOU
on
15 Aug 2017
at
11:30

Type: Speaker

Competing point forecasts for functionals such as the mean, a quantile, or a certain risk measure are commonly compared in terms of loss functions. These should be incentive compatible, i.e., the expected score should be minimized by the correctly specified functional of interest. A functional is called *elicitable* if it possesses such an incentive compatible loss function. With the squared loss
... More

Presented by Dr. Tobias FISSLER
on
15 Aug 2017
at
11:00

Type: Speaker

We define heavy-tailed fractional reciprocal gamma and Fisher-Snedecor diffusions by a non-Markovian time change in the corresponding Pearson diffusions. We illustrate known theoretical results regarding these fractional diffusions via simulations.

Presented by Mr. Ivan PAPIĆ
on
18 Aug 2017
at
10:30

Type: Speaker

My talk is based on ongoing joint work with my supervisor Jüri Lember.
We consider a Markov chain $Z = \{Z_k\}_{k \geq 1}$ with product
state space $\mathcal{X}\times \mathcal{Y}$, where $\mathcal{Y}$ is
a finite set (state space) and $\mathcal{X}$ is an arbitrary
separable metric space (observation space). Thus, the process $Z$
decomposes as $Z=(X,Y)$, where $X=\{X_k \}_{k\geq 1}$ and $Y=
... More

Presented by Mr. Joonas SOVA
on
17 Aug 2017
at
10:00