An adaptive lack of fit test for big data

New technological advancements combined with powerful computer hardware and high-speed network make big data available. The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods. In this paper, we focus on the lack of fit test of...
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Autor*in:	Yanyan Zhao [verfasserIn] Changliang Zou [verfasserIn] Zhaojun Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	adaptive test asymptotic distribution divide-and-conquer algorithm massive dataset model specification test

Übergeordnetes Werk:	In: Statistical Theory and Related Fields - Taylor & Francis Group, 2022, 1(2017), 1, Seite 59-68
Übergeordnetes Werk:	volume:1 ; year:2017 ; number:1 ; pages:59-68

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

DOI / URN:	10.1080/24754269.2017.1339373

Katalog-ID:	DOAJ095102531

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allfieldsSound	10.1080/24754269.2017.1339373 doi (DE-627)DOAJ095102531 (DE-599)DOAJ328bea8e4c2e4158a2e370271579e9d0 DE-627 ger DE-627 rakwb eng QA273-280 Yanyan Zhao verfasserin aut An adaptive lack of fit test for big data 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier New technological advancements combined with powerful computer hardware and high-speed network make big data available. The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods. In this paper, we focus on the lack of fit test of parametric regression models under the framework of big data. We develop a computationally feasible testing approach via integrating the divide-and-conquer algorithm into a powerful nonparametric test statistic. Our theory results show that under mild conditions, the asymptotic null distribution of the proposed test is standard normal. Furthermore, the proposed test benefits from the use of data-driven bandwidth procedure and thus possesses certain adaptive property. Simulation studies show that the proposed method has satisfactory performances, and it is illustrated with an analysis of an airline data. adaptive test asymptotic distribution divide-and-conquer algorithm massive dataset model specification test Probabilities. Mathematical statistics Changliang Zou verfasserin aut Zhaojun Wang verfasserin aut In Statistical Theory and Related Fields Taylor & Francis Group, 2022 1(2017), 1, Seite 59-68 (DE-627)1001172221 24754277 nnns volume:1 year:2017 number:1 pages:59-68 https://doi.org/10.1080/24754269.2017.1339373 kostenfrei https://doaj.org/article/328bea8e4c2e4158a2e370271579e9d0 kostenfrei http://dx.doi.org/10.1080/24754269.2017.1339373 kostenfrei https://doaj.org/toc/2475-4269 Journal toc kostenfrei https://doaj.org/toc/2475-4277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 1 2017 1 59-68
language	English
source	In Statistical Theory and Related Fields 1(2017), 1, Seite 59-68 volume:1 year:2017 number:1 pages:59-68
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topic_facet	adaptive test asymptotic distribution divide-and-conquer algorithm massive dataset model specification test Probabilities. Mathematical statistics
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authorswithroles_txt_mv	Yanyan Zhao @@aut@@ Changliang Zou @@aut@@ Zhaojun Wang @@aut@@
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callnumber-first	Q - Science
author	Yanyan Zhao
spellingShingle	Yanyan Zhao misc QA273-280 misc adaptive test misc asymptotic distribution misc divide-and-conquer algorithm misc massive dataset misc model specification test misc Probabilities. Mathematical statistics An adaptive lack of fit test for big data
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topic_title	QA273-280 An adaptive lack of fit test for big data adaptive test asymptotic distribution divide-and-conquer algorithm massive dataset model specification test
topic	misc QA273-280 misc adaptive test misc asymptotic distribution misc divide-and-conquer algorithm misc massive dataset misc model specification test misc Probabilities. Mathematical statistics
topic_unstemmed	misc QA273-280 misc adaptive test misc asymptotic distribution misc divide-and-conquer algorithm misc massive dataset misc model specification test misc Probabilities. Mathematical statistics
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title	An adaptive lack of fit test for big data
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title_auth	An adaptive lack of fit test for big data
abstract	New technological advancements combined with powerful computer hardware and high-speed network make big data available. The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods. In this paper, we focus on the lack of fit test of parametric regression models under the framework of big data. We develop a computationally feasible testing approach via integrating the divide-and-conquer algorithm into a powerful nonparametric test statistic. Our theory results show that under mild conditions, the asymptotic null distribution of the proposed test is standard normal. Furthermore, the proposed test benefits from the use of data-driven bandwidth procedure and thus possesses certain adaptive property. Simulation studies show that the proposed method has satisfactory performances, and it is illustrated with an analysis of an airline data.
abstractGer	New technological advancements combined with powerful computer hardware and high-speed network make big data available. The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods. In this paper, we focus on the lack of fit test of parametric regression models under the framework of big data. We develop a computationally feasible testing approach via integrating the divide-and-conquer algorithm into a powerful nonparametric test statistic. Our theory results show that under mild conditions, the asymptotic null distribution of the proposed test is standard normal. Furthermore, the proposed test benefits from the use of data-driven bandwidth procedure and thus possesses certain adaptive property. Simulation studies show that the proposed method has satisfactory performances, and it is illustrated with an analysis of an airline data.
abstract_unstemmed	New technological advancements combined with powerful computer hardware and high-speed network make big data available. The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods. In this paper, we focus on the lack of fit test of parametric regression models under the framework of big data. We develop a computationally feasible testing approach via integrating the divide-and-conquer algorithm into a powerful nonparametric test statistic. Our theory results show that under mild conditions, the asymptotic null distribution of the proposed test is standard normal. Furthermore, the proposed test benefits from the use of data-driven bandwidth procedure and thus possesses certain adaptive property. Simulation studies show that the proposed method has satisfactory performances, and it is illustrated with an analysis of an airline data.
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code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="h">59-68</subfield></datafield></record></collection>
score	7.401017

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