First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts

In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS)...
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Autor*in:	Jie Zhang [verfasserIn] Dehui Wang [verfasserIn] Kai Yang [verfasserIn] Xiaogang Dong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	multinomial autoregressive process random coefficient binomial thinning parameter estimation

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 13(2021), 12, p 2271
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:12, p 2271

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym13122271

Katalog-ID:	DOAJ084762470

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allfieldsSound	10.3390/sym13122271 doi (DE-627)DOAJ084762470 (DE-599)DOAJ8fdc531ac09f4caba67b29b558c7e3c1 DE-627 ger DE-627 rakwb eng QA1-939 Jie Zhang verfasserin aut First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model. multinomial autoregressive process random coefficient binomial thinning parameter estimation Mathematics Dehui Wang verfasserin aut Kai Yang verfasserin aut Xiaogang Dong verfasserin aut In Symmetry MDPI AG, 2009 13(2021), 12, p 2271 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:13 year:2021 number:12, p 2271 https://doi.org/10.3390/sym13122271 kostenfrei https://doaj.org/article/8fdc531ac09f4caba67b29b558c7e3c1 kostenfrei https://www.mdpi.com/2073-8994/13/12/2271 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 12, p 2271
language	English
source	In Symmetry 13(2021), 12, p 2271 volume:13 year:2021 number:12, p 2271
sourceStr	In Symmetry 13(2021), 12, p 2271 volume:13 year:2021 number:12, p 2271
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	multinomial autoregressive process random coefficient binomial thinning parameter estimation Mathematics
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authorswithroles_txt_mv	Jie Zhang @@aut@@ Dehui Wang @@aut@@ Kai Yang @@aut@@ Xiaogang Dong @@aut@@
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callnumber-first	Q - Science
author	Jie Zhang
spellingShingle	Jie Zhang misc QA1-939 misc multinomial autoregressive process misc random coefficient misc binomial thinning misc parameter estimation misc Mathematics First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts
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topic_title	QA1-939 First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts multinomial autoregressive process random coefficient binomial thinning parameter estimation
topic	misc QA1-939 misc multinomial autoregressive process misc random coefficient misc binomial thinning misc parameter estimation misc Mathematics
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title	First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts
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title_sort	first-order random coefficient multinomial autoregressive model for finite-range time series of counts
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title_auth	First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts
abstract	In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model.
abstractGer	In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model.
abstract_unstemmed	In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model.
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First-Order Random Coefficient Multinomial Autoregressive Model for Finite-Range Time Series of Counts

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