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A test for stationarity for irregularly spaced spatial data

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  • Soutir Bandyopadhyay
  • Suhasini Subba Rao

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  • Soutir Bandyopadhyay & Suhasini Subba Rao, 2017. "A test for stationarity for irregularly spaced spatial data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 95-123, January.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:1:p:95-123
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    File URL: http://hdl.handle.net/10.1111/rssb.12161
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    References listed on IDEAS

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    1. Fuentes, Montserrat, 2005. "A formal test for nonstationarity of spatial stochastic processes," Journal of Multivariate Analysis, Elsevier, vol. 96(1), pages 30-54, September.
    2. Kim, Hyoung-Moon & Mallick, Bani K. & Holmes, C.C., 2005. "Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 653-668, June.
    3. Yongtao Guan & Michael Sherman & James A. Calvin, 2004. "A Nonparametric Test for Spatial Isotropy Using Subsampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 810-821, January.
    4. Montserrat Fuentes, 2002. "Spectral methods for nonstationary spatial processes," Biometrika, Biometrika Trust, vol. 89(1), pages 197-210, March.
    5. Yasumasa Matsuda & Yoshihiro Yajima, 2009. "Fourier analysis of irregularly spaced data on Rd," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 191-217, January.
    6. Jentsch, Carsten & Subba Rao, Suhasini, 2015. "A test for second order stationarity of a multivariate time series," Journal of Econometrics, Elsevier, vol. 185(1), pages 124-161.
    7. Yogesh Dwivedi & Suhasini Subba Rao, 2011. "A test for second‐order stationarity of a time series based on the discrete Fourier transform," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(1), pages 68-91, January.
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    Cited by:

    1. Tingjin Chu & Jialuo Liu & Jun Zhu & Haonan Wang, 2022. "Spatio-Temporal Expanding Distance Asymptotic Framework for Locally Stationary Processes," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 689-713, August.
    2. Lee, Sangyeol & Meintanis, Simos G. & Pretorius, Charl, 2022. "Monitoring procedures for strict stationarity based on the multivariate characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    3. Marco H. Benedetti & Veronica J. Berrocal & Naveen N. Narisetty, 2022. "Identifying regions of inhomogeneities in spatial processes via an M‐RA and mixture priors," Biometrics, The International Biometric Society, vol. 78(2), pages 798-811, June.
    4. Nesbitt, Peter & Blake, Lewis R. & Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo K. & Newman, Alexandra & Brickey, Andrea, 2021. "Underground mine scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 294(1), pages 340-352.
    5. Esmail Yarali & Firoozeh Rivaz, 2020. "Incorporating covariate information in the covariance structure of misaligned spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    6. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
    7. Liu, Jialuo & Chu, Tingjin & Zhu, Jun & Wang, Haonan, 2021. "Semiparametric method and theory for continuously indexed spatio-temporal processes," Journal of Multivariate Analysis, Elsevier, vol. 183(C).

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