Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2020.3023008 ◽

2020 ◽

Vol 68 ◽

pp. 5260-5275 ◽

Author(s):

Feng Yin ◽

Lishuo Pan ◽

Tianshi Chen ◽

Sergios Theodoridis ◽

Zhi-Quan Tom Luo ◽

...

Keyword(s):

Time Series ◽

Gaussian Processes ◽

Low Rank ◽

Stationary Gaussian Processes

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Sampling Reconstruction Procedure for Non-Stationary Gaussian Processes Based on Conditional Expectation Rule

Sampling Theory, Signal Processing, and Data Analysis ◽

10.1007/bf03549374 ◽

2002 ◽

Vol 1 (2) ◽

pp. 135-153

Author(s):

V.A. Kazakov ◽

M. A. Belyaev

Keyword(s):

Gaussian Processes ◽

Conditional Expectation ◽

Reconstruction Procedure ◽

Stationary Gaussian Processes

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Fourth moment bound and stationary Gaussian processes with positive correlation

Journal of the Korean Statistical Society ◽

10.1007/s42952-021-00132-6 ◽

2021 ◽

Author(s):

Yoon Tae Kim ◽

Hyun Suk Park

Keyword(s):

Gaussian Processes ◽

Fourth Moment ◽

Positive Correlation ◽

Stationary Gaussian Processes

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Limit Theorems for Stationary Gaussian Processes

Theory of Probability and Its Applications ◽

10.1137/1110014 ◽

1965 ◽

Vol 10 (1) ◽

pp. 132-139 ◽

Author(s):

Yu M. Ryzhov

Keyword(s):

Gaussian Processes ◽

Limit Theorems ◽

Stationary Gaussian Processes

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Frequency-domain fatigue analysis of wide-band stationary Gaussian processes using a trimodal spectral formulation

International Journal of Fatigue ◽

10.1016/j.ijfatigue.2008.01.008 ◽

2008 ◽

Vol 30 (10-11) ◽

pp. 1944-1955 ◽

Author(s):

Z GAO ◽

T MOAN

Keyword(s):

Frequency Domain ◽

Gaussian Processes ◽

Wide Band ◽

Fatigue Analysis ◽

Stationary Gaussian Processes

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Some superconcentration inequalities for extrema of stationary Gaussian processes

Statistics & Probability Letters ◽

10.1016/j.spl.2015.07.028 ◽

2015 ◽

Vol 106 ◽

pp. 239-246 ◽

Author(s):

Kevin Tanguy

Keyword(s):

Gaussian Processes ◽

Stationary Gaussian Processes

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An algorithm for on-the-fly generation of samples of non-stationary Gaussian processes based on a sampling theorem

Monte Carlo Methods and Applications ◽

10.1515/mcma-2013-0004 ◽

2013 ◽

Vol 0 (0) ◽

Author(s):

Richard V. Field, Jr. ◽

Mircea Grigoriu ◽

Clark R. Dohrmann

Keyword(s):

Gaussian Processes ◽

Sampling Theorem ◽

Stationary Gaussian Processes

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Time series models for non-Gaussian processes

Institute of Mathematical Statistics Lecture Notes - Monograph Series - Topics in statistical dependence ◽

10.1214/lnms/1215457550 ◽

1990 ◽

pp. 69-83 ◽

Author(s):

H. W. Block ◽

N. A. Langberg ◽

D. S. Stoffer

Keyword(s):

Time Series ◽

Gaussian Processes ◽

Time Series Models ◽

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Persistent Spins in the Linear Diffusion Approximation of Phase Ordering and Zeros of Stationary Gaussian Processes

Physical Review Letters ◽

10.1103/physrevlett.77.2871 ◽

1996 ◽

Vol 77 (14) ◽

pp. 2871-2874 ◽

Author(s):

Bernard Derrida ◽

Vincent Hakim ◽

Reuven Zeitak

Keyword(s):

Gaussian Processes ◽

Diffusion Approximation ◽

Linear Diffusion ◽

Phase Ordering ◽

Stationary Gaussian Processes

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An Erd?s-R�v�sz type law of the iterated logarithm for stationary Gaussian processes

Probability Theory and Related Fields ◽

10.1007/bf01222513 ◽

1992 ◽

Vol 94 (1) ◽

pp. 119-133 ◽

Author(s):

Qi-Man Shao

Keyword(s):

Gaussian Processes ◽

Iterated Logarithm ◽

Stationary Gaussian Processes

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Bounds for the expected supremum of some non-stationary Gaussian processes

Stochastics ◽

10.1080/17442508.2021.2018445 ◽

2021 ◽

pp. 1-23

Author(s):

Hossein Jafari ◽

Yiqiang Q. Zhao

Keyword(s):

Gaussian Processes ◽

Stationary Gaussian Processes

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