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Published in:

Volume 5 Issue 10
October-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1810A34


Registration ID:
305108

Page Number

896-908

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Title

Selection of Biasing Scalar in Adaptive Ridge Regression Estimator: Concentration Probability Approach

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Abstract

Concentration optimality of various constituent member estimators of the family of Ridge Regression estimators has been explored by deriving the large sample approximations for the density of the family. These density approximations are utilized to evaluate the concentration probability approximations for the estimator being close to the unknown coefficient vector which, in turn, are compared to derive the respective dominance conditions for the superiority of these Ridge Regression estimators over each other.

Key Words

Ridge Regression, Estimation, Concentration Optimality

Cite This Article

"Selection of Biasing Scalar in Adaptive Ridge Regression Estimator: Concentration Probability Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.896-908, October-2018, Available :http://www.jetir.org/papers/JETIR1810A34.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Selection of Biasing Scalar in Adaptive Ridge Regression Estimator: Concentration Probability Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp896-908, October-2018, Available at : http://www.jetir.org/papers/JETIR1810A34.pdf

Publication Details

Published Paper ID: JETIR1810A34
Registration ID: 305108
Published In: Volume 5 | Issue 10 | Year October-2018
DOI (Digital Object Identifier):
Page No: 896-908
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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