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Parametric design and multiple objective intelligent optimization of marine nuclear turbine aerothermodynamics under variable conditions. [PDF]
Sci RepZhang L, Chen G, Shi YA, Xie L.
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Disjunctive kriging, universal kriging, or no kriging: Small sample results with simulated fields
Mathematical Geology, 1986This paper provides a comparison between linear (universal) and nonlinear (disjunctive) kriging estimators when they are computed from small samples chosen randomly on simulated stationary and nonstationary fields. Point estimation results are reported.
Carlos E. Puente, Rafael L. Bras
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2019
This chapter discusses universal kriging, the kriging of a random function Z(x) which is not intrinsic and exhibits an expectation E [Z(x)] = m(x) variable over the space. For the function m(x), called the drift of Z(x), a model has to be chosen, usually polynomial of degree 1, 2, or 3.
Guojun Gan, Emiliano A. Valdez
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This chapter discusses universal kriging, the kriging of a random function Z(x) which is not intrinsic and exhibits an expectation E [Z(x)] = m(x) variable over the space. For the function m(x), called the drift of Z(x), a model has to be chosen, usually polynomial of degree 1, 2, or 3.
Guojun Gan, Emiliano A. Valdez
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Mathematical Geology, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Lognormal Kriging: Bias Adjustment and Kriging Variances
2005Lognormality of spatial data occurs commonly enough for it to warrant continued study; contemporary statistical and computational methodologies can shed new light on the old problem of block kriging for lognormal processes. There are a number of proposals available for block kriging, many of them discussed in an unpublished, 43-page, Centre de ...
Noel Cressie, Martina Pavlicová
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2019
Chapter 4 discusses kriging; one calls kriging, or simple kriging, of the random function Y in a panel P the best linear estimator Y K of Y by N samples Y α. This optimum is given by starting from the extension variance of Y − Y K, where Y K = λ α
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Chapter 4 discusses kriging; one calls kriging, or simple kriging, of the random function Y in a panel P the best linear estimator Y K of Y by N samples Y α. This optimum is given by starting from the extension variance of Y − Y K, where Y K = λ α
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