Results 71 to 80 of about 17,823 (205)

Adaptive Estimation for Weakly Dependent Functional Times Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under 𝕃p−m‐approximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro, Valentin Patilea, Myriam Vimond   +2 more
wiley   +1 more source

Statistics of voltage fluctuations in resistively shunted Josephson junctions

, 2010
The intrinsic nonlinearity of Josephson junctions converts Gaussian current noise in the input into non-Gaussian voltage noise in the output. For a resistively shunted Josephson junction with white input noise we determine numerically exactly the ...
A. Barone, D. S. Golubev, Gerd Schön, K. K. Likharev, K. K. Likharev, M. Marthaler, Y. Utsumi   +6 more
core   +1 more source

On Testing for Independence Between Generalized Error Models of Several Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi, Bouchra R. Nasri, Bruno N. Rémillard   +2 more
wiley   +1 more source

Exact results at the 2-D percolation point

, 1997
We derive exact expressions for the excess number of clusters b and the excess cumulants b_n of a related quantity at the 2-D percolation point. High-accuracy computer simulations are in accord with our predictions.
A. Aharony, A. E. Ferdinand, C.-K. Hu, G. Watts, H. Kesten, H. N. V. Temperley, H. Pinson, H. Saleur, H. W. J. Blöte, I. Affleck, J. C. Cardy, J. Cardy, J. L. Cardy, J.-P. Hovi, P. di Francesco, P. Kleban, P. Kleban, P. Kleban, P. Kleban, P. W. Kasteleyn, R. J. Baxter, R. J. Baxter, R. Langlands, R. M. Ziff, R. M. Ziff, V. S. Dotsenko   +25 more
core   +1 more source

Higher order pion interferometry. Moments and cumulants

Physics Letters B, 1993
Abstract Pion interferometry can be viewed as intermittency analysis in terms of momentum transfer of like-sign particles. By putting the common practice of event mixing on a sound footing and extending its use, we provide the tools to eliminate the combinatoric background contained in the usual correlation functions. Information on true higher order
H.C. Eggers, P. Lipa, P. Carruthers, B. Buschbeck   +3 more
openaire   +1 more source

Volume fluctuations and higher-order cumulants of the net baryon number [PDF]

Physical Review C, 2013
5 pages, 3 figures; version published in ...
Skokov, V., Friman, B., Redlich, K.
openaire   +2 more sources

Least Trimmed Squares: Cointegration and Outliers

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT When applying the cointegrated autoregressive distributed lag model it is common to include indicator variables for outliers. This is often done in a somewhat ad hoc way. Least Trimmed Squares estimation provides a more systematic approach. This estimator is robust to a large number of outliers of many types.
Vanessa Berenguer‐Rico, Bent Nielsen
wiley   +1 more source

Cumulants in the 3-dimensional Ising, O(2) and O(4) spin models

, 2013
Based on the universal properties of a critical point in different systems and that the QCD phase transitions fall into the same universality classes as the 3-dimensional Ising, $O(2)$ or $O(4)$ spin models, the critical behavior of cumulants and higher ...
Chen, Lizhu, Chen, X. S., Pan, Xue, Wu, Yuanfang   +3 more
core   +1 more source

Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio [PDF]

Radioengineering, 2019
In this paper, new algorithms for spectrum sensing in cognitive radio based on higher order cumulants and kurtosis are proposed. The cumulants represent statistical signal processing based on pattern recognition for signals of different structure, and ...
R. Bozovic, M. Simic
doaj  

Linear Causal Disentanglement via Higher-Order Cumulants

La Matematica
Abstract Linear causal disentanglement (LCD) is a recent method in causal representation learning to describe a collection of observed variables via latent variables with causal dependencies between them. It can be viewed as a generalization of both independent component analysis and linear structural equation models.
Paula Leyes Carreno, Chiara Meroni, Anna Seigal   +2 more
openaire   +2 more sources