## A method for computing least squares estimators that keep up with the data |

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12 n th A R C adjoining an n+1 analysis of variance ARCON based upon n+1 combination of h components are z(l conditional expectation constraints GX covariance defined inductively diag dimensional Euclidean distance h A h h B h h n n-1 HP(Ht identity matrix Kalman least squares estimator lemma 1(c let H Let z(l linear combination linear regression linear subspace m-dimensional vectors minimize 3.1 minimum norm n n n-1 n-1 n n n n-vector n+1 n n+1 n+1 n+1 n+1 n+1 observations normal distributions numbers and let nxm matrix obtained by adjoining orthogonal matrix otherwise problem of minimizing Proof real numbers row is h row vector satisfies 2.1 sequence of m-dimensional sequence of real sequential analysis set of X's solution to 2.1 spanned by g theorem 3.1 vectors h z(n+l zero mean normal