23 research outputs found
Interaction patterns of brain activity across space, time and frequency. Part I: methods
We consider exploratory methods for the discovery of cortical functional
connectivity. Typically, data for the i-th subject (i=1...NS) is represented as
an NVxNT matrix Xi, corresponding to brain activity sampled at NT moments in
time from NV cortical voxels. A widely used method of analysis first
concatenates all subjects along the temporal dimension, and then performs an
independent component analysis (ICA) for estimating the common cortical
patterns of functional connectivity. There exist many other interesting
variations of this technique, as reviewed in [Calhoun et al. 2009 Neuroimage
45: S163-172]. We present methods for the more general problem of discovering
functional connectivity occurring at all possible time lags. For this purpose,
brain activity is viewed as a function of space and time, which allows the use
of the relatively new techniques of functional data analysis [Ramsay &
Silverman 2005: Functional data analysis. New York: Springer]. In essence, our
method first vectorizes the data from each subject, which constitutes the
natural discrete representation of a function of several variables, followed by
concatenation of all subjects. The singular value decomposition (SVD), as well
as the ICA of this new matrix of dimension [rows=(NT*NV); columns=NS] will
reveal spatio-temporal patterns of connectivity. As a further example, in the
case of EEG neuroimaging, Xi of size NVxNW may represent spectral density for
electric neuronal activity at NW discrete frequencies from NV cortical voxels,
from the i-th EEG epoch. In this case our functional data analysis approach
would reveal coupling of brain regions at possibly different frequencies.Comment: Technical report 2011-March-15, The KEY Institute for Brain-Mind
Research Zurich, KMU Osak
Group Lasso estimation of high-dimensional covariance matrices
In this paper, we consider the Group Lasso estimator of the covariance matrix
of a stochastic process corrupted by an additive noise. We propose to estimate
the covariance matrix in a high-dimensional setting under the assumption that
the process has a sparse representation in a large dictionary of basis
functions. Using a matrix regression model, we propose a new methodology for
high-dimensional covariance matrix estimation based on empirical contrast
regularization by a group Lasso penalty. Using such a penalty, the method
selects a sparse set of basis functions in the dictionary used to approximate
the process, leading to an approximation of the covariance matrix into a low
dimensional space. Consistency of the estimator is studied in Frobenius and
operator norms and an application to sparse PCA is proposed
Nonparametric estimation of covariance functions by model selection
We propose a model selection approach for covariance estimation of a
multi-dimensional stochastic process. Under very general assumptions, observing
i.i.d replications of the process at fixed observation points, we construct an
estimator of the covariance function by expanding the process onto a collection
of basis functions. We study the non asymptotic property of this estimate and
give a tractable way of selecting the best estimator among a possible set of
candidates. The optimality of the procedure is proved via an oracle inequality
which warrants that the best model is selected
Adaptive estimation of spectral densities via wavelet thresholding and information projection
In this paper, we study the problem of adaptive estimation of the spectral
density of a stationary Gaussian process. For this purpose, we consider a
wavelet-based method which combines the ideas of wavelet approximation and
estimation by information projection in order to warrants that the solution is
a nonnegative function. The spectral density of the process is estimated by
projecting the wavelet thresholding expansion of the periodogram onto a family
of exponential functions. This ensures that the spectral density estimator is a
strictly positive function. Then, by Bochner's theorem, the corresponding
estimator of the covariance function is semidefinite positive. The theoretical
behavior of the estimator is established in terms of rate of convergence of the
Kullback-Leibler discrepancy over Besov classes. We also show the excellent
practical performance of the estimator in some numerical experiments
A non parametric approach for calibration with functional data
International audienceA new nonparametric approach for statistical calibration with functional data is studied. The practical motivation comes from calibration problems in chemometrics in which a scalar random variable Y needs to be predicted from a functional random variable X. The proposed predictor takes the form of a weighted average of the observed values of Y in the training data set, where the weights are determined by the conditional probability density of X given Y. This functional density, which represents the data generation mechanism in the context of calibration , is so incorporated as a key information into the estimator. The new proposal is computationally simple and easy to implement. Its statistical consistency is proved, and its relevance is shown through simulations and an application to data
A Simulation Study of Functional Density-Based Inverse Regression
National audienceIn this paper a new nonparametric functional method is introduced for predicting a scalar random variable on the basis of a functional random variable . The prediction has the form of a weighted average of the training data , where the weights are determined by the conditional probability density of given , which is assumed to be Gaussian. In this way such a conditional probability density is incorporated as a key information into the estimator. Contrary to some previous approaches, no assumption about the dimensionality of or about the distribution of is required. The new proposal is computationally simple and easy to implement. Its performance is assessed through a simulation study
A functional density-based nonparametric approach for statistical calibration
International audienceIn this paper a new nonparametric functional method is introduced for predicting a scalar random variable from a functional random variable . The resulting prediction has the form of a weighted average of the training data set, where the weights are determined by the conditional probability density of given , which is assumed to be Gaussian. In this way such a conditional probability density is incorporated as a key information into the estimator. Contrary to some previous approaches, no assumption about the dimensionality of is required. The new proposal is computationally simple and easy to implement. Its performance is shown through its application to both simulated and real data
Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG "leakage correction"
The problem of interest here is the study of brain functional and effective
connectivity based on non-invasive EEG-MEG inverse solution time series. These
signals generally have low spatial resolution, such that an estimated signal at
any one site is an instantaneous linear mixture of the true, actual, unobserved
signals across all cortical sites. False connectivity can result from analysis
of these low-resolution signals. Recent efforts toward "unmixing" have been
developed, under the name of "leakage correction". One recent noteworthy
approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which
forces the inverse solution signals to have zero cross-correlation at lag zero.
One goal is to show that Colclough's method produces false human connectomes
under very broad conditions. The second major goal is to develop a new
solution, that appropriately "unmixes" the inverse solution signals, based on
innovations orthogonalization. The new method first fits a multivariate
autoregression to the inverse solution signals, giving the mixed innovations.
Second, the mixed innovations are orthogonalized. Third, the mixed and
orthogonalized innovations allow the estimation of the "unmixing" matrix, which
is then finally used to "unmix" the inverse solution signals. It is shown that
under very broad conditions, the new method produces proper human connectomes,
even when the signals are not generated by an autoregressive model.Comment: preprint, technical report, under license
"Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND
4.0)", https://creativecommons.org/licenses/by-nc-nd/4.0
Modélisation de séries financières, estimation, ajustement de modèles et test d'hypothèses
De nombreux modèles ont été proposés pour expliquer la dynamique complexe des marchés financiers. La plupart des problèmes en finance comme la valorisation d'options, la couverture d'options, l'optimisation de porte-feuille, etc. ont été étudiés dans le cadre de ces nouveaux modèles. Mais pour les applications il est nécessaire de disposer de méthodes précises pour ajuster ces modèles aux données réelles. Dans cette thèse nous étudions plusieurs problèmes inférentiels relatifs aux modèles communément utilisés en finance. Le premier chapitre de cette thèse est une révision commentée de la majorité des méthodes existantes pour estimer les paramètres des lois stables. Entre autres, la méthode appelée de L-moments est de notre crû. Les propriétés de ces méthodes ont été testés sur des données simulées. Finalement nous présentons une application à l'estimation de la VaR (Value at Risk) sur des données financières réelles. Dans le deuxième chapitre nous étudions le problème de l'estimation d'un modèle de diffusion avec sauts observé à temps discret. Nous proposons deux méthodes différentes pour résoudre ce problème inférentiel, et nous prouvons que après une détection préalable des sauts, nous pouvons estimer les paramètres d'une diffusion avec sauts en utilisant des méthodes similaires a celles utilisées dans le cas des diffusions ordinaires. Un étude de simulation vient confirmer les résultats théoriques obtenus. Dans le troisième chapitre nous considérons le problème de la détection des sauts de la volatilité dans un modèle à volatilité stochastique. Nous proposons des estimateurs pour le nombre des sauts de la volatilité, des instants de saut et de la volatilité entre les sauts. Nous démontrons enfin un résultat asymptotique sur ces estimateurs lorsque le pas de discrétisation décroît vers 0.TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF