Influence Measures in Ridge Linear Measurement Error Models
author
Abstract:
Usually the existence of influential observations is complicated by the presence of collinearity in linear measurement error models. However no method of influence measure available for the possible effect's that collinearity can have on the influence of an observation in such models. In this paper, a new type of ridge estimator based corrected likelihood function (REC) for linear measurement error models is defined. We show when this type of ridge estimator is used to mitigate the effects of collinearity the influence of some observations can be drastically modified. We propose a case deletion formula to detect influential points in REC. As an illustrative example two real data set are analysed.
similar resources
Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models
In this article we consider the stochastic restricted ridge estimation in semipara-metric linear models when the covariates are measured with additive errors. The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates. The asymptotic normality of the resulting estimates are established. Also, necessary and sufficient condition...
full textDetection of Outliers and Influential Observations in Linear Ridge Measurement Error Models with Stochastic Linear Restrictions
The aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. In addition, we derive the corrected score test statistic for outliers detection ba...
full textdetection of outliers and influential observations in linear ridge measurement error models with stochastic linear restrictions
the aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. in addition, we derive the corrected score test statistic for outliers detection ba...
full textA New Ridge Estimator in Linear Measurement Error Model with Stochastic Linear Restrictions
In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...
full textLocal influence in measurement error models with ridge estimate
Ridge estimate has been suggested as an alternative to the maximum likelihood estimate in the presence of collinearity among the elements of unobservable values in measurement error models. This paper studies the local influence of minor perturbations on the ridge estimate in the measurement error model. The diagnostics under the perturbation of variance and explanatory variables are considered...
full textDiagnostic measures for linear mixed measurement error models
In this paper, we present case deletion and mean shift outlier models for linear mixed measurement error models using the corrected likelihood of Nakamura (1990). We derive the corrected score test statistic for outliers detection based on mean shift outlier models. Furthermore, several case deletion diagnostics are constructed as a tool for influence diagnostics. It is found that they can be w...
full textMy Resources
Journal title
volume 12 issue 1
pages 39- 56
publication date 2015-09
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023