finite observation
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scholarly journals Optimal Sequential Tests for Monitoring Changes in the Distribution of Finite Observation Sequences

2022 ◽  
Author(s):  
Dong Han ◽  
Fugee Tsung ◽  
Jinguo Xian ◽  
Miaomiao Yu
Keyword(s):  
Sequential Tests ◽  
Finite Observation

scholarly journals Synchronization Measure Based on a Geometric Approach to Attractor Embedding Using Finite Observation Windows

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Inga Timofejeva ◽  
Kristina Poskuviene ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Time Delays ◽  
Geometric Approach ◽  
Optimal Time ◽  
Effective Algorithm ◽  
Geometrical Properties ◽  
Finite Observation

A simple and effective algorithm for the identification of optimal time delays based on the geometrical properties of the embedded attractor is presented in this paper. A time series synchronization measure based on optimal time delays is derived. The approach is based on the comparison of optimal time delay sequences that are computed for segments of the considered time series. The proposed technique is validated using coupled chaotic Rössler systems.

scholarly journals Estimating interevent time distributions from finite observation periods in communication networks

2015 ◽  
Vol 92 (5) ◽  
Author(s):  
Mikko Kivelä ◽  
Mason A. Porter
Keyword(s):  
Communication Networks ◽  
Interevent Time ◽  
Finite Observation

Multi-resolution Fourier Analysis: achieved high resolutions with suppressed finite observation effects

2015 ◽  
Vol 10 (4) ◽  
pp. 711-718
Author(s):  
Nourédine Yahya Bey
Keyword(s):  
Fourier Analysis ◽  
Finite Observation

Finite Observation Models

2010 ◽  
pp. 255-270
Author(s):  
A. W. Roscoe
Keyword(s):  
Finite Observation

scholarly journals On processes which cannot be distinguished by finite observation

2008 ◽  
Vol 164 (1) ◽  
pp. 265-284 ◽  
Author(s):  
Yonatan Gutman ◽  
Michael Hochman
Keyword(s):  
Finite Observation

scholarly journals MSE of the best linear predictor in nonorthogonal models

2007 ◽  
Vol 57 (1) ◽  
Author(s):  
František Štulajter
Keyword(s):  
Time Series ◽  
White Noise ◽  
Mean Squared Error ◽  
Linear Predictor ◽  
Squared Error ◽  
Additive White Noise ◽  
Best Linear Predictor ◽  
The Mean ◽  
Finite Observation ◽  
Noise Models

AbstractThe problem of computing the mean squared error (MSE) of the best linear predictor (BLP) in finite discrete spectrum with an additive white noise models(FDSWNMs) for an observed time series is considered. This is done under the assumption that the corresponding vectors in models for finite observation of this time series are not orthogonal.

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