951 resultados para Residual variance
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Bibliography: p. 100.
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Issued Oct. 1977.
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Mode of access: Internet.
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"July 2000."
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Includes bibliographical references (p. 56-57).
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Literature cited: p. 24-26.
Multivariate analyses of variance and covariance for simulation studies involving normal time series
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Photocopy.
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Performing organization: Dept. of Statistics, University of Michigan.
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Sponsored jointly by the Welding Research Council and the Department of the Navy.
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Spatial characterization of non-Gaussian attributes in earth sciences and engineering commonly requires the estimation of their conditional distribution. The indicator and probability kriging approaches of current nonparametric geostatistics provide approximations for estimating conditional distributions. They do not, however, provide results similar to those in the cumbersome implementation of simultaneous cokriging of indicators. This paper presents a new formulation termed successive cokriging of indicators that avoids the classic simultaneous solution and related computational problems, while obtaining equivalent results to the impractical simultaneous solution of cokriging of indicators. A successive minimization of the estimation variance of probability estimates is performed, as additional data are successively included into the estimation process. In addition, the approach leads to an efficient nonparametric simulation algorithm for non-Gaussian random functions based on residual probabilities.
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It is shown that variance-balanced designs can be obtained from Type I orthogonal arrays for many general models with two kinds of treatment effects, including ones for interference, with general dependence structures. These designs can be used to obtain optimal and efficient designs. Some examples and design comparisons are given. (C) 2002 Elsevier B.V. All rights reserved.