Nonparametric testing for the specification of spatial trend functions
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 105180 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 196 |
Online published | 18 Mar 2023 |
Publication status | Published - Jul 2023 |
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Abstract
Correct specification of a spatial trend constitutes an important topic in statistics because it facilitates spatial kriging and inference. This paper proposes a global integrated squared error (GISE) statistics between the nonparametric smoothing surface and the parametric hypothesized model to test for the goodness-of-fit of spatial trends. By virtue of the m-dependence approximation of a stationary random field, it is shown that under certain regularity conditions, the proposed GISE statistics has an asymptotic normal distribution. Further, a grid-based block bootstrap (GBBB) procedure is also proposed to deal with the complicated asymptotic variance involved in the limit distribution. Numerical studies are also presented to illustrate the performance of the proposed method. © 2023 Elsevier Inc.
Research Area(s)
- Goodness-of-fit, Grid-based block bootstrap, m-dependence approximation, Nonparametric smoothing, Spatial trends
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Nonparametric testing for the specification of spatial trend functions. / Zhang, Rongmao; Chan, Ngai Hang; Chi, Changxiong.
In: Journal of Multivariate Analysis, Vol. 196, 105180, 07.2023.
In: Journal of Multivariate Analysis, Vol. 196, 105180, 07.2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review