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题名Orthogonality and D-optimality of the U-type design under general Fourier regression models
作者
发表日期2007-07-01
发表期刊Statistics and Probability Letters
ISSN/eISSN0167-7152
卷号77期号:12页码:1377-1384
摘要

Riccomagno et al. [1997. Lattice-based D-optimum design for Fourier regression. Ann. Statist. 25, 2313-2317] gave a comprehensive study on D-optimality under a symmetric Fourier regression model and gave a characterization on orthogonality, complete orthogonality and D-optimality of lattice designs under a symmetrical Fourier model. This paper extends their results to asymmetric Fourier regression models. We give some necessary condition for the orthogonality and complete orthogonality of U-type designs under general Fourier regression models that involve symmetrical and asymmetrical Fourier models and prove that a U-type design, which is completely orthogonal, is not D-optimal for an asymmetrical Fourier model. © 2007.

关键词Fourier regression model Good lattice points Optimum design Orthogonality Uniform design
DOI10.1016/j.spl.2007.03.024
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收录类别SCIE
语种英语English
WOS研究方向Mathematics
WOS类目Statistics & Probability
WOS记录号WOS:000247835400026
Scopus入藏号2-s2.0-34249941552
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/1765
专题理工科技学院
通讯作者Fang, Kaitai
作者单位
1.Department of Mathematics and Statistics, Huazhong Normal University, Wuhan, 430079, China
2.BNU-HKBU United International College, Zhuhai Campus Beijing Normal University, Zhuhai, 519085, 28 Jinfeng Rd., China
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Xie, Minyu,Ning, Jianhui,Fang, Kaitai. Orthogonality and D-optimality of the U-type design under general Fourier regression models[J]. Statistics and Probability Letters, 2007, 77(12): 1377-1384.
APA Xie, Minyu, Ning, Jianhui, & Fang, Kaitai. (2007). Orthogonality and D-optimality of the U-type design under general Fourier regression models. Statistics and Probability Letters, 77(12), 1377-1384.
MLA Xie, Minyu,et al."Orthogonality and D-optimality of the U-type design under general Fourier regression models". Statistics and Probability Letters 77.12(2007): 1377-1384.
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