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Nonlinear Lp-Norm Estimation

List Price: $87.99
SKU:
9780367451165
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  • Product Details

    Author:
    Rene Gonin
    Format:
    Paperback
    Pages:
    316
    Publisher:
    CRC Press (December 18, 2020)
    Language:
    English
    ISBN-13:
    9780367451165
    Weight:
    16oz
    Dimensions:
    6" x 9"
    File:
    TAYLORFRANCIS-TayFran_260405043614355-20260405.xml
    Folder:
    TAYLORFRANCIS
    List Price:
    $87.99
    Series:
    Statistics: A Series of Textbooks and Monographs
    Case Pack:
    1
    As low as:
    $83.59
    Publisher Identifier:
    P-CRC
    Discount Code:
    H
    Audience:
    Professional and scholarly
    Country of Origin:
    United States
    Pub Discount:
    30
    Imprint:
    CRC Press
  • Overview

    Complete with valuable FORTRAN programs that help solve nondifferentiable nonlinear LtandLo.-norm estimation problems, this important reference/text extensively delineates ahistory of Lp-norm estimation. It examines the nonlinear Lp-norm estimation problem that isa viable alternative to least squares estimation problems where the underlying errordistribution is nonnormal, i.e., non-Gaussian.Nonlinear LrNorm Estimation addresses both computational and statistical aspects ofLp-norm estimation problems to bridge the gap between these two fields . . . contains 70useful illustrations ... discusses linear Lp-norm as well as nonlinear Lt, Lo., and Lp-normestimation problems . . . provides all appropriate computational algorithms and FORTRANlistings for nonlinear Lt- and Lo.-norm estimation problems . . . guides readers with clear endof-chapter notes on related topics and outstanding research publications . . . contains numericalexamples plus several practical problems .. . and shows how the data can prescribe variousapplications of Lp-norm alternatives.Nonlinear Lp-Norm Estimation is an indispensable reference for statisticians,operations researchers, numerical analysts, applied mathematicians, biometricians, andcomputer scientists, as well as a text for graduate students in statistics or computer science.