[Statlist] Friday, April 8, 2016 with Rainer von Sachs (Université catholique de Louvain)

Maurer Letizia |et|z|@m@urer @end|ng |rom ethz@ch
Mon Apr 4 12:20:26 CEST 2016


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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf
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We are glad to announce the following talk:

Friday, April 8, 2016 at 15.15h  ETH Zurich HG G 19.141
with Rainer von Sachs (Université catholique de Louvain)
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Title:


Functional mixed effect models for spectra of

subject-replicated time series

Abstract:


In this work in progress we treat a functional mixed effects model in the setting of spectral

analysis of subject-replicated time series data. We assume that the time series subjects share a common

population spectral curve (functional fixed effect), additional to some random subject-specific deviation

around this curve (functional random effects), which models the variability within the population. In

contrast to existing work we allow this variability to be non-diagonal, i.e. there may exist explicit correlation

between the different subjects in the population.

To estimate the common population curve we project the subject-curves onto an appropriate orthonormal

basis (such as a wavelet basis) and continue working in the coefficient domain instead of the functional

domain. In a sampled data model, with discretely observed noisy subject-curves, the model in the coefficient

domain reduces to a finite-dimensional linear mixed model. This allows us, for estimation and

prediction of the fixed and random effect coefficients, to apply both traditional linear mixed model methods

and, if necessary by the spatially variable nature of the spectral curves, work with some appropriate

non-linear thresholding approach.

We derive some theoretical properties of our methodology highlighting the influence of the correlation

in the subject population. To illustrate the proposed functional mixed model, we show some examples

using simulated time series data, and an analysis of empirical subject-replicated EEG data.

We conclude with some possible extensions, among which we allow situations where the data show potential

breakpoints in its second order (spectral) structure over time.

The presented work is joint with Joris Chau (ISBA, UCL).



This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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