Complete with valuable FORTRAN programs that help solve nondifferentiable nonlinear LtandLo.-norm estimation problems, this important reference/text extensively delineates a history of Lp-norm estimation.

It examines the nonlinear Lp-norm estimation problem that is a viable alternative to least squares estimation problems where the underlying error distribution is nonnormal, i.e., non-Gaussian.

Nonlinear Lp-Norm Estimation addresses both computational and statistical aspects of Lp-norm estimation problems to bridge the gap between these two fields.

Contains 70 useful illustrations ... discusses linear Lp-norm as well as nonlinear Lt, Lo., and Lp-norm estimation problems ... provides all appropriate computational algorithms and FORTRAN listings for nonlinear Lt- and Lo.-norm estimation problems ... guides readers with clear end-of-chapter notes on related topics and outstanding research publications ... contains numerical examples plus several practical problems ... and shows how the data can prescribe various applications of Lp-norm alternatives.

Nonlinear Lp-Norm Estimation is an indispensable reference for statisticians, operations researchers, numerical analysts, applied mathematicians, biometricians, and computer scientists, as well as a text for graduate students in statistics or computer science.