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GEOMETRICAL PROPERTIES OF OPTIMAL ESTIMATORS
GAUSS METHOD OF LEAST SQUARES
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application assumed asymptotic Chapter coefficients column space component compute conditional expectation consider constant convergence corresponding covariance matrix decision defined denote derived diagonal differential equation distribution function elements equivalent estimate error estimation problems estimation theory exists expected value formulated Fourier Gauss given hypothesis independent initial estimate inverse iteration least squares estimate likelihood equation likelihood function linear estimator linear filter loss function mean value measurement method minimal data set noise nonlinear normally distributed observations obtained operator optimal estimator optimum filter orthogonal orthonormal polynomial positive definite postulated prediction function probability density function pseudoinverse quadratic random process random variables random vectors regression relation restriction result risk function sample satisfies Section selected sequence sequential estimator signal s(t solution statistical Suppose symmetric system parameters Taylor series technique Theorem transformation true value unbiased estimate W-K theory Wiener-Hopf equation written yields zero