Outliers in a statistical analysis strongly affect the performance of the ordinary least squares, such outliers need to be detected and extreme outliers deleted. Thisp is aimed at proposing a Redescending M-estimator which is more efficient and robust compared to other existing methods. The results show that the proposed method is effective in detection and deletion of extreme outliers compared to the other existing ones
In this article,a new robust ratio type estimator using the Uk’s redescending M-estimator isproposed...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Standard statistical techniques such as least squares regression are very accurate if the underlying...
In this paper we present a new redescending M-estimator “Insha’s estimator†for robust regressi...
A new P-function is proposed in the family of smoothly redescending M-estimators. The Shi-function ...
M-estimators are used as a robust replacement of the general classical estimators used in the field ...
We propose a procedure for computing a fast approximation to regression estimates based on the minim...
It is evident from the comments by Bernoulli (1777) that the history of outliers is very old and tra...
Regression analysis is one of the most important branches of multivariate statistical techniques. It...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
The problems of outliers detection and robust regression in a high-dimensional setting are fundament...
In this article, an estimation procedure to simple linear regression in the presence of outliers is ...
In this paper, we present a new algorithm for detecting multiple outliers in linear regression. The ...
Information criteria for model choice are extended to the detection of outliers in regression models...
In this article,a new robust ratio type estimator using the Uk’s redescending M-estimator isproposed...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Standard statistical techniques such as least squares regression are very accurate if the underlying...
In this paper we present a new redescending M-estimator “Insha’s estimator†for robust regressi...
A new P-function is proposed in the family of smoothly redescending M-estimators. The Shi-function ...
M-estimators are used as a robust replacement of the general classical estimators used in the field ...
We propose a procedure for computing a fast approximation to regression estimates based on the minim...
It is evident from the comments by Bernoulli (1777) that the history of outliers is very old and tra...
Regression analysis is one of the most important branches of multivariate statistical techniques. It...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
The problems of outliers detection and robust regression in a high-dimensional setting are fundament...
In this article, an estimation procedure to simple linear regression in the presence of outliers is ...
In this paper, we present a new algorithm for detecting multiple outliers in linear regression. The ...
Information criteria for model choice are extended to the detection of outliers in regression models...
In this article,a new robust ratio type estimator using the Uk’s redescending M-estimator isproposed...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Standard statistical techniques such as least squares regression are very accurate if the underlying...