Add to ORKGMultiple outliers are frequently encountered in applied studies in business and economics. Most of the practitioners depend on ordinary least squares (OLS) method for parameter estimation in regression analysis without identifying outliers properly. It is evident that OLS totally fails even in presence of single outlying observation. Single observation outlier detection methods are failed to identify multiple outliers due to masking and swamping effects. This paper analytically and numerically compares the sensitivity of the most popular diagnostic statistics. Data set from Griliches and Lichtenberg (1984) is used to show that we need to take extra care for model building process in presence of multiple outliers
Abstract: An outlier is an observation that deviates markedly from the majority of the data. To know...
The presence of outliers can contribute to serious deviance in findings of statistical models. In th...
A number of methods are available to detect outliers in univariate data sets. Most of these tests ar...
Abstract This paper describes a procedure for identifying multiple outliers in linear regression. Th...
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
The problem of outliers in statistical data has attracted many researchers for a long time. Conseque...
Regression analysis is one of the most important branches of multivariate statistical techniques. It...
Studying the observations in regression analysis it is seen that the out-put of regression is affect...
Regression analysis is one of the most important branches of multivariate statistical techniques. It...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
This study attempts to investigate the effect of outliers on estimation of parameters in regression ...
In this paper, we present a new algorithm for detecting multiple outliers in linear regression. The ...
In the last decades many statistical tests based on the least squares solution have been proposed fo...
Abstract: An outlier is an observation that deviates markedly from the majority of the data. To know...
The presence of outliers can contribute to serious deviance in findings of statistical models. In th...
A number of methods are available to detect outliers in univariate data sets. Most of these tests ar...
Abstract This paper describes a procedure for identifying multiple outliers in linear regression. Th...
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
The problem of outliers in statistical data has attracted many researchers for a long time. Conseque...
Regression analysis is one of the most important branches of multivariate statistical techniques. It...
Studying the observations in regression analysis it is seen that the out-put of regression is affect...
Regression analysis is one of the most important branches of multivariate statistical techniques. It...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
This study attempts to investigate the effect of outliers on estimation of parameters in regression ...
In this paper, we present a new algorithm for detecting multiple outliers in linear regression. The ...
In the last decades many statistical tests based on the least squares solution have been proposed fo...
Abstract: An outlier is an observation that deviates markedly from the majority of the data. To know...
The presence of outliers can contribute to serious deviance in findings of statistical models. In th...
A number of methods are available to detect outliers in univariate data sets. Most of these tests ar...