Linear sufficiency and linear admissibility in a continuous time Gauss-Markov model.

DOI10.1016/S0047-259X(03)00128-3zbMath1040.62074MaRDI QIDQ1426350

Ana Pérez-Palomares, Ibarrola Pilar

Publication date: 14 March 2004

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

Gauss-Markov modelContinuous timeRidge estimatorLinear admissibilityLinear sufficiency

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Non-Markovian processes: estimation (62M09) Admissibility in statistical decision theory (62C15) Sufficient statistics and fields (62B05)

Related Items (4)

Best linear unbiased prediction for linear combinations in general mixed linear models ⋮ Best Quadratic Unbiased Prediction in a General Linear Model with Stochastic Regression Coefficients ⋮ Linear completeness in a continuous time Gauss-Markov model ⋮ A characterization of a Gaussian process in terms of sufficient estimators

Cites Work

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• Estimation of parameters in a linear model
• Weighted least squares estimates in linear regression models for processes with uncorrelated increments
• Sufficiency and completeness in the linear model
• Characterization of general ridge estimators
• On sequential estimation of the mean of a multidimensional gaussian process
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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