Minimum Lp Norm Estimator for Simple Linear Regressive Model

We consider the simple linear regressive model, whose error processes are decrescent. The minimum Lp(p greater than or equal to 1) norm estimator of the unknown parameter for this model is investigated and the consistency and asymptotic distribution are also obtained.

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 40; no. 4-6; pp. 571 - 580
Main Authors Miao, Yu, Yang, Guangyu, Mu, Jianyong
Format Journal Article
LanguageEnglish
Published Philadelphia, PA Taylor & Francis 2011
Subjects
Asymptotic properties
Consistency
Distribution theory
Errors
Estimators
Exact sciences and technology
General topics
Mathematical models
Mathematics
Norms
Probability and statistics
Sciences and techniques of general use
Statistics
norm estimate
62J05
Statistical distribution
Error estimation
Consistency
Simple linear regressive model
Asymptotic distribution
Asymptotic convergence
Linear model
Statistical method
Linear process
Auto-regression model
Regression model
62F12
Minimum L
62E17
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Summary:We consider the simple linear regressive model, whose error processes are decrescent. The minimum Lp(p greater than or equal to 1) norm estimator of the unknown parameter for this model is investigated and the consistency and asymptotic distribution are also obtained.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0361-0926
1532-415X
DOI:10.1080/03610920903391311