An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions

The paper presents an approximate algorithm for modeling a stationary discrete random process with marginal and bivariate distributions of its consecutive components in the form of a mixture of two Gaussian distributions. The algorithm is based on a combination of the conditional distribution method...

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Veröffentlicht in:Numerical analysis and applications Jg. 17; H. 2; S. 169 - 173
Hauptverfasser: Ogorodnikov, V. A., Akenteva, M. S., Kargapolova, N. A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.06.2024
Springer Nature B.V
Schlagworte:
Accuracy
Air temperature
Algorithms
Bivariate analysis
Computational mathematics
Conflicts of interest
Estimates
Mathematics
Mathematics and Statistics
Mixtures
Normal distribution
Numerical Analysis
Random processes
Random variables
Simulation
Temperature
Time series
Russia
bivariate distribution
mixture of Gaussian distributions
maximum daily temperature
stochastic simulation
ISSN:1995-4239, 1995-4247
Online-Zugang:Volltext
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Zusammenfassung:The paper presents an approximate algorithm for modeling a stationary discrete random process with marginal and bivariate distributions of its consecutive components in the form of a mixture of two Gaussian distributions. The algorithm is based on a combination of the conditional distribution method and the rejection method. An example of application of the proposed algorithm for simulating time series of daily maximum air temperatures is given.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1995-4239
1995-4247
DOI:10.1134/S199542392402006X