article Open AccessTop 10% cited

Why your model parameter confidences might be too optimistic. Unbiased estimation of the inverse covariance matrix

Astronomy and Astrophysics · 2006 · Vol. 464(1) · pp. 399–404

J. Hartlap✉(University of Bonn)P. Šimon(University of Bonn)Petra Schneider(University of Bonn)

Abstract

Aims.The maximum-likelihood method is the standard approach to obtain model fits to observational data and the corresponding confidence regions. We investigate possible sources of bias in the log-likelihood function and its subsequent analysis, focusing on estimators of the inverse covariance matrix. Furthermore, we study under which circumstances the estimated covariance matrix is invertible.

Statistical and numerical algorithms Statistical Methods and Bayesian Inference Climate variability and modelsEstimation of covariance matricesMathematicsCovariance matrixStatisticsCovarianceEstimatorLikelihood functionInvertible matrixCovariance functionApplied mathematics

Citations

840

FWCI

9.57

field-weighted impact

References

Percentile

99%

vs. same field & year

Citations per year

Cited by

The clustering of the SDSS DR7 main Galaxy sample – I. A 4 per cent distance measure at z = 0.15