Results 211 to 220 of about 779,832 (257)

Consistency of Estimates of Variance Components

Biometrics, 1951
THE ESTIMATION OF VARIANCE COMPONENTS has important applications in genetic research, and geneticists were among the first to use analysis of variance extensively for that purpose. Early examples of component estimation in genetics are contained in papers by Lush et al. (1934), Bywaters (1937), and Stonaker and Lush (1942).
R E, COMSTOCK, H F, ROBINSON
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Nonlinear Component of Variance Models

Biometrika, 1992
SUMMARY General aspects of nonlinearity in the context of component of variance models are discussed, and two special topics are examined in detail. Firstly, simple procedures, both formal and informal, are proposed for describing departures from normal-theory linear models.
Solomon, P. J., Cox, D. R.
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COMPONENTS OF VARIANCE MODELS WITH TRANSFORMATIONS

Australian Journal of Statistics, 1996
SummaryPower transformations are a popular way to improve the agreement between the observations and the assumptions in a statistical model. In this paper it is assumed that the data, after appropriate power transformation Λ, satisfies a variance components model, with independent Gaussian components. The focus is on inference for quantities which have
Taylor, Jeremy M. G., Cumberland, William G., Meng, Xiangyi   +2 more
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Extraction of Components with Structured Variance

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
We present a method for exploratory data analysis of large spatiotemporal data sets such as global longtime climate measurements, extending our previous work on semiblind source separation of climate data. The method seeks fast changing components whose variances exhibit slow behavior with specific temporal structure.
Alexander Ilin, Harri Valpola, Erkki Oja
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A Note on Variances in the Components of Variance Model

The American Statistician, 1971
In a components of variance model the variance of individual observations and of various averages of observations are shown to be dependent upon the design-that is, upon the sampling scheme. For example, using the usual notation, for the nested random model yij = iA+ ri + eJ(i), i = 1, 2, . . . I, and j = 1 2, ... J, Var(ysi) = [(I 1)J/(IJ 1)]a2 + a2e.
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Variance-Importance of System Components

IEEE Transactions on Reliability, 1982
The paper defines the variance-importance of a component as the product of a relative sensitivity coefficient of component variance and the variance of component-unreliability estimate. Variance-importance is useful for identifying components that appreciably contribute to the uncertainty of system unreliability.
Nakashima, Kyoichi, Yamato, Kazuharu
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Nonlinear Regression With Variance Components

Journal of the American Statistical Association, 1992
Abstract The nonlinear model with variance components, which combines a nonlinear model for the mean with additive random effects, is applicable to split-plot and nested experiments. We propose two methods of estimation for the parameters of the nonlinear model for the mean: (1) estimated generalized least squares (EGLS), and (2) maximum likelihood ...
Marcia L. Gumpertz, Sastry G. Pantula
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Variance Components.

The Statistician, 1994
Neil Spencer, S. R. Searle, G. Casella, C. E. McCulloch   +3 more
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Components of variance

1981
So far, the examples of analysis-of-variance problems that we have considered all fall within the scope of what we call the ‘fixed effects’ analysis, or the ‘model I’ situation. In these problems the questions being asked are questions about contrasts of sets of means.
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Statistical Analysis of Negative Variance Components in the Estimation of Variance Components

2008
This paper analyzes the problem of negative variance components in the estimation of variance components from the statistical point of view, based on two kinds of estimators of variance components. Our analysis shows that too low relative accuracy and too large errors of the estimated variance components in comparison with the variance component itself
B. Gao, S. Li, W. Li, S. Li, X. Wang
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