Statistical Estimation of Parameters for Binary Conditionally Nonlinear Autoregressive Time Series

Kharin, Yu. S.; Voloshko, Valeriy A.; Medved, Eva

doi:10.3103/s1066530718020023

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…the bilinear basis catched some essential high-order statistical dependencies invisible to the linear one. These identified properties of the bilinear models confirm the known in genetics effect of statistical 3-periodicity of protein-coding fragments (Kharin et al 2018); this fact indicates the usefulness of the developed theory for statistical analysis of genetic sequences.…”

Section: Analysis Of Cg-patterns In Genomesupporting

confidence: 75%

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Statistical Analysis of Discrete-valued Time Series by Parsimonious High-order Markov Chains

Kharin¹

2020

AJS

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Problems of statistical analysis of discrete-valued time series are considered. Two approaches for construction of parsimonious (small-parametric) models for observed discrete data are proposed based on high-order Markov chains.Consistent statistical estimators for parameters of the developed models and some known models, and also statistical tests on the values of parameters are constructed. Probabilistic properties of the constructed statistical inferences are given. The developed theory is also applied for statistical analysis of spatio-temporal data. Theoretical results are illustrated by computer experiments on real statistical data.

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Section: Analysis Of Cg-patterns In Genomesupporting

confidence: 75%

“…, T . Portion of "1" in X T 1 is known as CG-content and plays important role in bioinformatics (Kharin et al 2018).…”

Section: Analysis Of Cg-patterns In Genomementioning

confidence: 99%

Statistical Analysis of Discrete-valued Time Series by Parsimonious High-order Markov Chains

Kharin¹

2020

AJS

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“…The FBE approach is successively developed for some other types of discrete-valued time series (19): semibinomial [80], binary [78,84], Poisson, geometric, negative binomial [75], regression time series [81], and also for Binomial conditionally nonlinear model for spatio-temporal data [85]; it is used for statistical forecasting of COVID-19, see [83], for more references.…”

Section: The Case Of Models Constructed By Approach IImentioning

confidence: 99%

“…is determined by the order s, the family E, and the function θ(q) : A s → Θ. The following special case of the latter function leads to the so-called Conditionally Nonlinear AutoRegressive (CNAR) model E-CNAR(s) [55,80,84]:…”

Section: Extensions For MDV Time Series Based On High-order Markov Ch...mentioning

confidence: 99%

Statistical analysis of multivariate discrete-valued time series

Fokianos

Fried

Kharin

et al. 2022

Journal of Multivariate Analysis

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Robust estimation for Binomial conditionally nonlinear autoregressive time series based on multivariate conditional frequencies

Kharin

Voloshko

2021

Journal of Multivariate Analysis

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Statistical Estimation of Parameters for Binary Conditionally Nonlinear Autoregressive Time Series

Cited by 10 publications

References 28 publications

Statistical Analysis of Discrete-valued Time Series by Parsimonious High-order Markov Chains

Statistical Analysis of Discrete-valued Time Series by Parsimonious High-order Markov Chains

Statistical analysis of multivariate discrete-valued time series

Robust estimation for Binomial conditionally nonlinear autoregressive time series based on multivariate conditional frequencies

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