P. P. Vaidyanathan received his bachelor of science, bachelor of technology, and master of technology degrees from the University of Calcutta, India. He obtained his doctor of philosophy degree from the University of California at Santa Barbara and became a faculty of the Department of Electrical Engineering at the California Institute of Technology in 1983. He served as the executive officer of the Department of ElectricalEngineering at Caltech for the period 2002-2005. He has received several awards for excellence in teaching at Caltech. He is the author of the book Multirate Systems and Filter Banks and has authored or coauthored more than 370 articles in the signal processing area. His research interests include digital filter banks, digital communications, image processing, genomic signal processing, and radar signal processing. He is an IEEE Fellow (1991), past distinguished lecturer of the IEEE SP society, and recipient of the IEEE ASSP Senior Paper Award and the S. K. Mitra Memorial Award (IETE, India). He was the Eliahu and Joyce Jury lecturer at the University of Miami for 2007. He received the F. E. Terman Award (ASEE) in 1995, the IEEE CAS Society's Golden Jubilee Medal in 1999, and the IEEE Signal Processing Society's Technical Achievement Award in 2001.

• Introduction.- The Optimal Linear Prediction Problem.- Levinson's Recursion.- Lattice Structures for Linear Prediction.- Autoregressive Modeling.- Prediction Error Bound and Spectral Flatness.- Line Spectral Processes.- Linear Prediction Theory for Vector Processes.- Appendix A: Linear Estimation of Random Variables.- B: Proof of a Property of Autocorrelations.- C: Stability of the Inverse Filter.- Recursion Satisfied by AR Autocorrelations.

P. P. Vaidyanathan received his bachelor of science, bachelor of technology, and master of technology degrees from the University of Calcutta, India. He obtained his doctor of philosophy degree from the University of California at Santa Barbara and became a faculty of the Department of Electrical Engineering at the California Institute of Technology in 1983. He served as the executive officer of the Department of ElectricalEngineering at Caltech for the period 2002-2005. He has received several awards for excellence in teaching at Caltech. He is the author of the book Multirate Systems and Filter Banks and has authored or coauthored more than 370 articles in the signal processing area. His research interests include digital filter banks, digital communications, image processing, genomic signal processing, and radar signal processing. He is an IEEE Fellow (1991), past distinguished lecturer of the IEEE SP society, and recipient of the IEEE ASSP Senior Paper Award and the S. K. Mitra Memorial Award (IETE, India). He was the Eliahu and Joyce Jury lecturer at the University of Miami for 2007. He received the F. E. Terman Award (ASEE) in 1995, the IEEE CAS Society's Golden Jubilee Medal in 1999, and the IEEE Signal Processing Society's Technical Achievement Award in 2001.

• Introduction.- The Optimal Linear Prediction Problem.- Levinson's Recursion.- Lattice Structures for Linear Prediction.- Autoregressive Modeling.- Prediction Error Bound and Spectral Flatness.- Line Spectral Processes.- Linear Prediction Theory for Vector Processes.- Appendix A: Linear Estimation of Random Variables.- B: Proof of a Property of Autocorrelations.- C: Stability of the Inverse Filter.- Recursion Satisfied by AR Autocorrelations.

P. P. Vaidyanathan received his bachelor of science, bachelor of technology, and master of technology degrees from the University of Calcutta, India. He obtained his doctor of philosophy degree from the University of California at Santa Barbara and became a faculty of the Department of Electrical Engineering at the California Institute of Technology in 1983. He served as the executive officer of the Department of ElectricalEngineering at Caltech for the period 2002-2005. He has received several awards for excellence in teaching at Caltech. He is the author of the book Multirate Systems and Filter Banks and has authored or coauthored more than 370 articles in the signal processing area. His research interests include digital filter banks, digital communications, image processing, genomic signal processing, and radar signal processing. He is an IEEE Fellow (1991), past distinguished lecturer of the IEEE SP society, and recipient of the IEEE ASSP Senior Paper Award and the S. K. Mitra Memorial Award (IETE, India). He was the Eliahu and Joyce Jury lecturer at the University of Miami for 2007. He received the F. E. Terman Award (ASEE) in 1995, the IEEE CAS Society's Golden Jubilee Medal in 1999, and the IEEE Signal Processing Society's Technical Achievement Award in 2001.

• Introduction.- The Optimal Linear Prediction Problem.- Levinson's Recursion.- Lattice Structures for Linear Prediction.- Autoregressive Modeling.- Prediction Error Bound and Spectral Flatness.- Line Spectral Processes.- Linear Prediction Theory for Vector Processes.- Appendix A: Linear Estimation of Random Variables.- B: Proof of a Property of Autocorrelations.- C: Stability of the Inverse Filter.- Recursion Satisfied by AR Autocorrelations.