Access the full text.
Sign up today, get DeepDyve free for 14 days.
M. Castro, M. Galea, H. Bolfarine (2007)
Local influence assessment in heteroscedastic measurement error modelsComput. Stat. Data Anal., 52
Joyce Wellman, R. Gunst (1991)
Influence diagnostics for linear measurement error modelsBiometrika, 78
R.D. Cook (1977)
Detection of influential observations in linear regressionTechnometrics, 19
D. Peña (2005)
A New Statistic for Influence in Linear RegressionTechnometrics, 47
Patricia Giménez, M. Galea (2013)
Influence measures on corrected score estimators in functional heteroscedastic measurement error modelsJ. Multivar. Anal., 114
Anwar Hossain, D. Naik (1991)
A comparative study on detection of influential observations in linear regressionStatistical Papers, 32
X. Zhong, B. Wei, W. Fung (2000)
Influence analysis for measurement error modelsAnn. Inst. Statist. Math, 52
(1998)
Analysis of Roman pottery from Colchester by inductively coupled plasma spectrometry
G. Seber (2007)
A matrix handbook for statisticians
(1987)
Measurements Error Models
K. Muller, Mario Mok (1997)
THE DISTRIBUTION OF COOK'S D STATISTIC.Communications in statistics: theory and methods, 26 3
(1990)
Corrected score functions for error in variables models: methodology and application to generalized linear models
D.M. Smith, F.A. Hart, R.D. Symond, J.N. Walsh (1998)
Science and Archaeology Glasgow
L.A. Stefanski, R.J. Carroll (1989)
Conditional scores and optimal scores for generalized measurement error modelsBiometrica, 74
Y. Zhao, A.H. Lee (1995)
Influence diagnostics for generalized measurement error modelsBiometrics, 50
G. Kelly (1981)
The influence function in the errors in variables problem
A. Rasekh, N. Fieller (2003)
INFLUENCE FUNCTIONS IN FUNCTIONAL MEASUREMENT ERROR MODELS WITH REPLICATED DATAStatistics, 37
D. Peña, V. Yohai (1995)
The Detection of Influential Subsets in Linear Regression by Using an Influence MatrixJournal of the royal statistical society series b-methodological, 57
Detection of multiple outliers or subset of influential points has been rarely considered in the linear measurement error models. In this paper a new influence statistic for one or a set of observations is generalized and characterized based on the corrected likelihood in the linear measurement error models. This influence statistic can be expressed in terms of the residuals and the leverages of linear measurement error regression. Unlike Cook’s statistic, this new measure of influence has asymptotically normal distribution and is able to detect a subset of high leverage outliers which is not identified by Cook’s statistic. As an illustrative example, simulation studies and a real data set are analysed.
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Aug 7, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.