Error estimates on averages of correlated data

1989 ◽  
Vol 91 (1) ◽  
pp. 461-466 ◽  
Author(s):  
H. Flyvbjerg ◽  
H. G. Petersen
Keyword(s):  
Error Estimates ◽  
Correlated Data

Systematic effects in observed parallaxes

1978 ◽  
Vol 48 ◽  
pp. 31-35
Author(s):  
R. B. Hanson
Keyword(s):  
Error Estimates ◽  
Zero Point ◽  
Absolute Zero ◽  
Apparent Magnitude ◽  
Coherent System ◽  
Preliminary Results ◽  
General Catalogue ◽  
Systematic Effects ◽  
New Evidence ◽  
Right Ascension

Several outstanding problems affecting the existing parallaxes should be resolved to form a coherent system for the new General Catalogue proposed by van Altena, as well as to improve luminosity calibrations and other parallax applications. Lutz has reviewed several of these problems, such as: (A) systematic differences between observatories, (B) external error estimates, (C) the absolute zero point, and (D) systematic observational effects (in right ascension, declination, apparent magnitude, etc.). Here we explore the use of cluster and spectroscopic parallaxes, and the distributions of observed parallaxes, to bring new evidence to bear on these classic problems. Several preliminary results have been obtained.

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

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