Results 251 to 260 of about 224,246 (306)

Performance of a wearable movement tracking system in detecting hypomobility in acute ischemic cerebrovascular events. [PDF]

BMC Biomed Eng
Ha DT, Nguyen VB, Vo ATT, Truong DT, Nguyen TV.   +4 more
europepmc   +1 more source

The dynamic urban river water quality prediction based on hybrid model. [PDF]

Sci Rep
Xu J, Song J, Yang Y, Ren R, Wang S, Lin H, Chen C.   +6 more
europepmc   +1 more source

Axonal Transport Deficits in Parkinson's Disease: Insights from Neurotoxin, Genetic, and Sporadic Models. [PDF]

Brain Sci
Wang X, Liu Z, Smith WW.
europepmc   +1 more source

Spring leaf-out keeps pace with warming across the Northern Hemisphere

Zohner C, Wu Z, Mo L, Crowther T, Fu Y, Renner S, Vitasse Y, Rebindaine D.   +7 more
europepmc   +1 more source

Moving Average Representations for Multivariate Stationary Processes

Journal of Time Series Analysis, 2006
Abstract.  Backward and forward moving average (MA) representations are established for multivariate stationary processes. It is observed that in the multivariate case, in contrast to the univariate case, the backward and forward MA coefficients correspondingly, in general, are different.
A R Soltani, M Mohammadpour
exaly   +4 more sources

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On prediction of moving-average processes

Advances in Applied Probability, 1980
Let {X n } be a discrete-time stationary moving-average process having the representation where the real-valued process (Y n ) has a well-defined entropy and spectrum. Let ∊∗2 k denote the smallest mean-squared error of any estimate of X n based on observations of X n–1 , X n–2 , …, X n–k , and let ∊∗2 klin , be the corresponding least mean-squared ...
Shepp, L. A., Slepian, D., Wyner, A. D.
openaire   +2 more sources

Moving average processes and maximum entropy

IEEE Transactions on Information Theory, 1992
Summary: A characterization of the stochastic process that has maximum entropy among all moving average processes of order \(q\), subject to the condition that the autovariances \(\gamma(k)\) satisfy \(\gamma(k)=c_ k\), for \(k=0,1,\ldots,p\), is provided by exploiting properties of the inverse autocovariance sequence.
Politis, Dimitris Nicolas, Politis, Dimitris Nicolas   +1 more
openaire   +4 more sources

THE SUM OF FINITE MOVING AVERAGE PROCESSES

Journal of Time Series Analysis, 1986
Abstract. It is well known that the sum of moving average processes is itself a moving average process. Existing theory does not provide formulae relating the innovations in the sum process to those in the component processes when some zeros of the autocovari‐ance function of the sum process are on the unit circle.
Darroch, John, Jiřina, Miloslav, McDonald, John   +2 more
openaire   +1 more source