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Least-squares fitting from the variogram cloud

Author

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  • Müller, Werner G.

Abstract

Several authors have noticed poor finite sample behavior of the most widely used parametric variogram estimator by Cressie (1985). This behavior is most notable in the case that fitting is done on the collection of squared differences of observations from all point pairs, the so-called variogram cloud. This can indeed be made plausible by asymptotical considerations and these motivate a corresponding simple correction. An alternative, even more efficient computer-intensive estimator is also proposed. The benefits of both methods are demonstrated by Monte-Carlo simulation.

Suggested Citation

  • Müller, Werner G., 1999. "Least-squares fitting from the variogram cloud," Statistics & Probability Letters, Elsevier, vol. 43(1), pages 93-98, May.
    • Handle: RePEc:eee:stapro:v:43:y:1999:i:1:p:93-98
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    Cited by:

    1. Tatiyana V. Apanasovich & David Ruppert & Joanne R. Lupton & Natasa Popovic & Nancy D. Turner & Robert S. Chapkin & Raymond J. Carroll, 2008. "Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data," Biometrics, The International Biometric Society, vol. 64(2), pages 490-500, June.
    2. Sun, Ying & Chang, Xiaohui & Guan, Yongtao, 2018. "Flexible and efficient estimating equations for variogram estimation," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 45-58.

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