349,680 research outputs found
Independent Process Analysis without A Priori Dimensional Information
Recently, several algorithms have been proposed for independent subspace
analysis where hidden variables are i.i.d. processes. We show that these
methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our
paper is that we introduce a cascade of algorithms, which aims to solve these
tasks without previous knowledge about the number and the dimensions of the
hidden processes. Our claim is supported by numerical simulations. As a
particular application, we search for subspaces of facial components.Comment: 9 pages, 2 figure
Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
Independent component analysis (ICA) has proven useful for modeling brain and
electroencephalographic (EEG) data. Here, we present a new, generalized method
to better capture the dynamics of brain signals than previous ICA algorithms.
We regard EEG sources as eliciting spatio-temporal activity patterns,
corresponding to, e.g., trajectories of activation propagating across cortex.
This leads to a model of convolutive signal superposition, in contrast with the
commonly used instantaneous mixing model. In the frequency-domain, convolutive
mixing is equivalent to multiplicative mixing of complex signal sources within
distinct spectral bands. We decompose the recorded spectral-domain signals into
independent components by a complex infomax ICA algorithm. First results from a
visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics
in the data, (2) links to subject behavior, (3) sources with a limited spectral
extent, and (4) a higher degree of independence compared to sources derived by
standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor
typo
Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits
A class of multivariate mixed survival models for continuous and discrete
time with a complex covariance structure is introduced in a context of
quantitative genetic applications. The methods introduced can be used in many
applications in quantitative genetics although the discussion presented
concentrates on longevity studies. The framework presented allows to combine
models based on continuous time with models based on discrete time in a joint
analysis. The continuous time models are approximations of the frailty model in
which the hazard function will be assumed to be piece-wise constant. The
discrete time models used are multivariate variants of the discrete relative
risk models. These models allow for regular parametric likelihood-based
inference by exploring a coincidence of their likelihood functions and the
likelihood functions of suitably defined multivariate generalized linear mixed
models. The models include a dispersion parameter, which is essential for
obtaining a decomposition of the variance of the trait of interest as a sum of
parcels representing the additive genetic effects, environmental effects and
unspecified sources of variability; as required in quantitative genetic
applications. The methods presented are implemented in such a way that large
and complex quantitative genetic data can be analyzed.Comment: 36 pages, 2 figures, 3 table
Estimation of Dynamic Mixed Double Factors Model in High Dimensional Panel Data
The purpose of this article is to develop the dimension reduction techniques
in panel data analysis when the number of individuals and indicators is large.
We use Principal Component Analysis (PCA) method to represent large number of
indicators by minority common factors in the factor models. We propose the
Dynamic Mixed Double Factor Model (DMDFM for short) to re ect cross section and
time series correlation with interactive factor structure. DMDFM not only
reduce the dimension of indicators but also consider the time series and cross
section mixed effect. Different from other models, mixed factor model have two
styles of common factors. The regressors factors re flect common trend and
reduce the dimension, error components factors re ect difference and weak
correlation of individuals. The results of Monte Carlo simulation show that
Generalized Method of Moments (GMM) estimators have good unbiasedness and
consistency. Simulation also shows that the DMDFM can improve prediction power
of the models effectively.Comment: 38 pages, 2 figure
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