英文论文
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文献类型
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Article
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题名
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Semiparametric spatio-temporal models with unknown and banded autoregressive coefficient matrices
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作者
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Wang, Hongxia; Luo, Xuehong; Ling, Long
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作者单位
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[Wang, Hongxia] Nanjing Audit Univ, Sch Stat & Math, Nanjing, Peoples R China. [Luo, Xuehong] Xiamen Univ, Sch Econ, Dept Stat & Data Sci, Xiamen, Peoples R China. [Ling, Long] Nanjing Normal Univ, Sch Business, Nanjing 210023, Peoples R China.
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通讯作者地址
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Nanjing Normal Univ, Sch Business, Nanjing 210023, Peoples R China.
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Email
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linglong0206@126.com
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ResearchID
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ORCID
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期刊名称
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MATHEMATICAL METHODS IN THE APPLIED SCIENCES
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出版社
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WILEY
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ISSN
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0170-4214
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出版信息
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2026-02, 49 (3):1666-1696.
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JCR
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2
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影响因子
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ISBN
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基金
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"Qinglan project" of Colleges and Universities of Jiangsu Province
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会议名称
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会议地点
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会议开始日期
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会议结束日期
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关键词
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local linear estimation; spatio-temporal autoregression; unknown and banded coefficient matrices; Yule-Walker equation
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摘要
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We consider a new class of semiparametric spatio-temporal models with unknown and banded autoregressive coefficient matrices. The setting represents a type of sparse structure in order to include as many panels as possible. We apply the local linear method and least squares method for Yule-Walker equation to estimate trend function and spatio-temporal autoregressive coefficient matrices respectively. We also balance the over-determined and under-determined phenomena in part by adjusting the order of extracting sample information. Both the asymptotic normality and convergence rates of the proposed estimators are established. We demonstrate, using both simulation and case studies, that the proposed estimators are stable among different sample sizes, and more efficient than the traditional method with known spatial weight matrices.
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一级学科
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Mathematics, Applied
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WOS入藏号
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WOS:001680854600028
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EI收录号
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20220111427445
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DOI
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10.1002/mma.8053
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ESI
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收录于
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SCIE, EI
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