Locally Most Powerful Test for the Random Coefficient Autoregressive Model

In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the lim...
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Autor*in:	Li Bi [verfasserIn] Feilong Lu [verfasserIn] Kai Yang [verfasserIn] Dehui Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2019)
Übergeordnetes Werk:	year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2019/6593821

Katalog-ID:	DOAJ042218497

Internformat


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520			\|a In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test.
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allfields	10.1155/2019/6593821 doi (DE-627)DOAJ042218497 (DE-599)DOAJ8b45cd8dc1b7494fbc7f661b52cb0f17 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Li Bi verfasserin aut Locally Most Powerful Test for the Random Coefficient Autoregressive Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test. Engineering (General). Civil engineering (General) Mathematics Feilong Lu verfasserin aut Kai Yang verfasserin aut Dehui Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2019) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2019 https://doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/article/8b45cd8dc1b7494fbc7f661b52cb0f17 kostenfrei http://dx.doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2019
spelling	10.1155/2019/6593821 doi (DE-627)DOAJ042218497 (DE-599)DOAJ8b45cd8dc1b7494fbc7f661b52cb0f17 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Li Bi verfasserin aut Locally Most Powerful Test for the Random Coefficient Autoregressive Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test. Engineering (General). Civil engineering (General) Mathematics Feilong Lu verfasserin aut Kai Yang verfasserin aut Dehui Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2019) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2019 https://doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/article/8b45cd8dc1b7494fbc7f661b52cb0f17 kostenfrei http://dx.doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2019
allfields_unstemmed	10.1155/2019/6593821 doi (DE-627)DOAJ042218497 (DE-599)DOAJ8b45cd8dc1b7494fbc7f661b52cb0f17 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Li Bi verfasserin aut Locally Most Powerful Test for the Random Coefficient Autoregressive Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test. Engineering (General). Civil engineering (General) Mathematics Feilong Lu verfasserin aut Kai Yang verfasserin aut Dehui Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2019) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2019 https://doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/article/8b45cd8dc1b7494fbc7f661b52cb0f17 kostenfrei http://dx.doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2019
allfieldsGer	10.1155/2019/6593821 doi (DE-627)DOAJ042218497 (DE-599)DOAJ8b45cd8dc1b7494fbc7f661b52cb0f17 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Li Bi verfasserin aut Locally Most Powerful Test for the Random Coefficient Autoregressive Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test. Engineering (General). Civil engineering (General) Mathematics Feilong Lu verfasserin aut Kai Yang verfasserin aut Dehui Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2019) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2019 https://doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/article/8b45cd8dc1b7494fbc7f661b52cb0f17 kostenfrei http://dx.doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2019
allfieldsSound	10.1155/2019/6593821 doi (DE-627)DOAJ042218497 (DE-599)DOAJ8b45cd8dc1b7494fbc7f661b52cb0f17 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Li Bi verfasserin aut Locally Most Powerful Test for the Random Coefficient Autoregressive Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test. Engineering (General). Civil engineering (General) Mathematics Feilong Lu verfasserin aut Kai Yang verfasserin aut Dehui Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2019) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2019 https://doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/article/8b45cd8dc1b7494fbc7f661b52cb0f17 kostenfrei http://dx.doi.org/10.1155/2019/6593821 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2019
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authorswithroles_txt_mv	Li Bi @@aut@@ Feilong Lu @@aut@@ Kai Yang @@aut@@ Dehui Wang @@aut@@
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spellingShingle	Li Bi misc TA1-2040 misc QA1-939 misc Engineering (General). Civil engineering (General) misc Mathematics Locally Most Powerful Test for the Random Coefficient Autoregressive Model
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title_sort	locally most powerful test for the random coefficient autoregressive model
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title_auth	Locally Most Powerful Test for the Random Coefficient Autoregressive Model
abstract	In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test.
abstractGer	In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test.
abstract_unstemmed	In this article, we study the problem of testing the constancy of the coefficient in a class of stationary first-order random coefficient autoregressive (RCAR(1)) model. We construct a new test statistic based on the locally most powerful-type (LMP) test. Under the null hypothesis, we derive the limiting distribution of the proposed test statistic. In the simulation, we compare the power between LMP test and empirical likelihood (EL) test and find that the accuracy of using LMP is 6.7%, 28.8%, and 26.1% higher than that of EL test under normal, student’s t, and symmetric contamination errors, respectively. A real life data is given to illustrate the practical effectiveness of our test.
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title_short	Locally Most Powerful Test for the Random Coefficient Autoregressive Model
url	https://doi.org/10.1155/2019/6593821 https://doaj.org/article/8b45cd8dc1b7494fbc7f661b52cb0f17 http://dx.doi.org/10.1155/2019/6593821 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147
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Locally Most Powerful Test for the Random Coefficient Autoregressive Model

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