Scharf, Louis L., authorDemeure, Cédric, authorIEEE, publisher2007-01-032007-01-031990Demeure, Cédric J. and Louis L. Scharf, Sliding Windows and Lattice Algorithms for Computing QR Factors in the Least Squares Theory of Linear Prediction, IEEE Transactions on Acoustics, Speech and Signal Processing 38, no. 4 (April 1990): 721-725.http://hdl.handle.net/10217/737In this correspondence we pose a sequence of linear prediction problems that differ a little from those previously posed. The solutions to these problems introduce a family of "sliding" window techniques into the least squares theory of linear prediction. By using these techniques we are able to QR factor the Toeplitz data matrices that arise in linear prediction. The matrix Q is an orthogonal version of the data matrix and the matrix R is a Cholesky factor of the experimental correlation matrix. Our QR and Cholesky algorithms generate generalized reflection coefficients that may be used in the usual ways for analysis, synthesis, or classification.born digitalarticleseng©1990 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.matrix algebraleast squares approximationsfiltering and prediction theoryText