✦ LIBER ✦

Covariance shaping least-squares estimation

✍ Scribed by Eldar, Y.C.; Oppenheim, A.V.

Book ID: 119821450
Publisher: IEEE
Year: 2003
Tongue: English
Weight: 774 KB
Volume: 51
Category: Article
ISSN: 1053-587X
DOI: 10.1109/tsp.2002.808125

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

On least squares estimation of generaliz

On least squares estimation of generalized covariance functions

✍ van der Linde, Angelika 📂 Article 📅 1993 🏛 Springer 🌐 English ⚖ 327 KB

Cauchy problem for least-squares estimat

Cauchy problem for least-squares estimation with semidegenerate covariance

✍ H. Natsuyama; S. Ueno 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 195 KB

A linear least-squares filtering problem with a semidegenerate integral equation is considered. An invariant imbedding treatment reduces this problem to a system of ordinary differential equations with initial conditions. The optimal estimate may be evaluated in a real-time sequential manner through

Generalized Least Squares Estimation of

Generalized Least Squares Estimation of a Genotypic Covariance Matrix

✍ P. Frederick Dahm, Bryan E. Melton and Wayne A. Fuller 📂 Article 📅 1983 🏛 John Wiley and Sons 🌐 English ⚖ 770 KB

Estimating a Genetic Covariance from Lea

Estimating a Genetic Covariance from Least Squares Solutions

✍ Wiggans, G.R.; Quaas, R.L.; Van Vleck, L.D. 📂 Article 📅 1980 🏛 American Dairy Science Association 🌐 English ⚖ 271 KB

Least-Squares Covariance Matrix Adjustme

Least-Squares Covariance Matrix Adjustment

✍ Boyd, Stephen; Xiao, Lin 📂 Article 📅 2005 🏛 Society for Industrial and Applied Mathematics 🌐 English ⚖ 198 KB

Least squares estimation of nonstationar

Least squares estimation of nonstationary covariance parameters in linear systems

✍ H.W. Brewer; C.T. Leondes 📂 Article 📅 1977 🏛 Elsevier Science 🌐 English ⚖ 1012 KB

Least squares estimation techniques are employed to overcome previous difficulties encountered in accurately estimating the state and measurement noise covariance parameters in linear stochastic systems. In the past accurate and rapidly converging covariance parameter estimates have been achieved wi