Search results for " normality"
showing 10 items of 16 documents
Separation of uncorrelated stationary time series using autocovariance matrices
2014
Blind source separation (BSS) is a signal processing tool, which is widely used in various fields. Examples include biomedical signal separation, brain imaging and economic time series applications. In BSS, one assumes that the observed $p$ time series are linear combinations of $p$ latent uncorrelated weakly stationary time series. The aim is then to find an estimate for an unmixing matrix, which transforms the observed time series back to uncorrelated latent time series. In SOBI (Second Order Blind Identification) joint diagonalization of the covariance matrix and autocovariance matrices with several lags is used to estimate the unmixing matrix. The rows of an unmixing matrix can be deriv…
Topology-based goodness-of-fit tests for sliced spatial data
2023
In materials science and many other application domains, 3D information can often only be extrapolated by taking 2D slices. In topological data analysis, persistence vineyards have emerged as a powerful tool to take into account topological features stretching over several slices. In the present paper, we illustrate how persistence vineyards can be used to design rigorous statistical hypothesis tests for 3D microstructure models based on data from 2D slices. More precisely, by establishing the asymptotic normality of suitable longitudinal and cross-sectional summary statistics, we devise goodness-of-fit tests that become asymptotically exact in large sampling windows. We illustrate the test…
Valutazione della bontà di una recente misura di ineguaglianza: uno studio di simulazione
2010
La misura di ineguaglianza proposta da Zenga (2007) per valutare il grado di concentrazione della ricchezza è valutata attraverso studi di simulazione. Tali studi riferiti anche come metodo di Monte Carlo, sono usati per investigare il comportamento di metodi statistici e misure sotto situazioni controllate, quando l’approccio analitico è complesso.
The squared symmetric FastICA estimator
2017
In this paper we study the theoretical properties of the deflation-based FastICA method, the original symmetric FastICA method, and a modified symmetric FastICA method, here called the squared symmetric FastICA. This modification is obtained by replacing the absolute values in the FastICA objective function by their squares. In the deflation-based case this replacement has no effect on the estimate since the maximization problem stays the same. However, in the symmetric case we obtain a different estimate which has been mentioned in the literature, but its theoretical properties have not been studied at all. In the paper we review the classic deflation-based and symmetric FastICA approaches…
On finite T-groups
2003
[EN] Characterisations of finite groups in which normality is a transitive relation are presented in the paper. We also characterise the finite groups in which every subgroup is either permutable or coincides with its permutiser as the groups in which every subgroup is permutable.
A Non-Normal Incidence Pumped Ni-Like Zr XRL for Spectroscopy of Li-Like Heavy Ions at GSI/FAIR
2008
One of the unique features of the PHELIX laser installation is the combination of the ultra-high intensity laser with the heavy-ion accelerator facility at GSI and its planned extension FAIR. Due to this combination, PHELIX will allow novel investigations in the fields of plasma physics, atomic physics, nuclear physics, and accelerator studies. An important issue within the scientific program is the generation of high quality x-ray laser beams for x-ray laser spectroscopy of highly-charged ions. The long range perspective is the study of nuclear properties of radioactive isotopes within the FAIR [1] project. A novel single mirror focusing scheme for the TCE XRL has been successfully impleme…
Rules and norms: two kinds of normative behaviour:
2016
Celano’s notion of a “pre-convention” is grounded in the opposition between two allegedly different kinds of normative behaviour: observing a “rule” and conforming to a “norm”. This opposition plays a central role in Celano’s paper, and marks a crucial point in his intellectual trajectory. Nevertheless, it remains largely implicit. In this paper, I try to make it fully explicit, giving a more precise characterisation of both kinds of normative behaviour. I also focus on the importance of distinguishing between them, express some conjectures (or wishes) regarding Celano’s future research, and propose a (marginal) criticism.
Local Asymptotic Normality for Shape and Periodicity in the Drift of a Time Inhomogeneous Diffusion
2017
We consider a one-dimensional diffusion whose drift contains a deterministic periodic signal with unknown periodicity $T$ and carrying some unknown $d$-dimensional shape parameter $\theta$. We prove Local Asymptotic Normality (LAN) jointly in $\theta$ and $T$ for the statistical experiment arising from continuous observation of this diffusion. The local scale turns out to be $n^{-1/2}$ for the shape parameter and $n^{-3/2}$ for the periodicity which generalizes known results about LAN when either $\theta$ or $T$ is assumed to be known.
Recursive estimation of the conditional geometric median in Hilbert spaces
2012
International audience; A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirm…
A more efficient second order blind identification method for separation of uncorrelated stationary time series
2016
The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel unmixing matrix estimator which selects the best lag set from a finite set of candidate sets specified by the user. The theory is illustrated by a simulation study.