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Nonparametric Bootstrap Estimation for Implicitly Weighted Robust Regression

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00475090" target="_blank" >RIV/67985807:_____/17:00475090 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Nonparametric Bootstrap Estimation for Implicitly Weighted Robust Regression

  • Original language description

    Implicitly weighted robust regression estimators for linear and nonlinear regression models include linear and nonlinear versions of the least trimmed squares and least weighted squares. After recalling known facts about these estimators, a nonparametric bootstrap procedure is proposed in this paper for estimates of their variances. These bootstrap estimates are elaborated for both the linear and nonlinear model. Practical contributions include several examples investigating the performance of the nonlinear least weighted squares estimator and comparing it with the classical least squares also by means of the variance estimates. Another theoretical novelty is a proposal of a two-stage version of the nonlinear least weighted squares estimator with adaptive (data-dependent) weights.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10103 - Statistics and probability

Result continuities

  • Project

    <a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings ITAT 2017: Information Technologies - Applications and Theory

  • ISBN

    978-1974274741

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    78-85

  • Publisher name

    Technical University & CreateSpace Independent Publishing Platform

  • Place of publication

    Aachen & Charleston

  • Event location

    Martinské hole

  • Event date

    Sep 22, 2017

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article