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Showing 4 results for Outliers
Forough Hajibagheri, Abdolrahman Rasekh, Mohammad Reza Akhoond,
Volume 8, Issue 1 (9-2014)
Abstract
The instability of the least squares parameter estimates under collinearity, might also causes instability of the residuals. If so, a large residual from a least squares fit might not be indicative of an erratic data point, and conversely. In order to resolve the problem of collinearity in the regression model, biased estimators like the Liu estimator is suggested. In this paper, it is shown that when Liu mean shift regression is used to mitigate the effect of the collinearity, the influence of some observations can be drastically changed and also the appropriate statistic for testing outliers is derived. In order to illustrate the performance of the proposed method, a real example is presented.
Reza Zabihi Moghadam, Rahim Chinipardaz, Gholamali Parham,
Volume 12, Issue 1 (9-2018)
Abstract
In this paper a method has been given to detect the shocks in structural time series using Kalman filter algorithm. As the Kalman filter algorithm is used for state space forms which include ARMA models as an especial case, the suggested method can be used for more general time series than linear models. Five shocks; additive outlier, level change, seasonal change, periodic change and slope change have been reviewed with this method. The performance of suggested method has been shown via a simulation study. The marriage data set from England has been considered as a real data set to study.
Mahdi Roozbeh, Morteza Amini,
Volume 13, Issue 2 (2-2020)
Abstract
In many fields such as econometrics, psychology, social sciences, medical sciences, engineering, etc., we face with multicollinearity among the explanatory variables and the existence of outliers in data. In such situations, the ordinary least-squares estimator leads to an inaccurate estimate. The robust methods are used to handle the outliers. Also, to overcome multicollinearity ridge estimators are suggested. On the other hand, when the error terms are heteroscedastic or correlated, the generalized least squares method is used. In this paper, a fast algorithm for computation of the feasible generalized least trimmed squares ridge estimator in a semiparametric regression model is proposed and then, the performance of the proposed estimators is examined through a Monte Carlo simulation study and a real data set.
Mahdi Roozbeh, Monireh Maanavi,
Volume 14, Issue 2 (2-2021)
Abstract
The popular method to estimation the parameters of a linear regression model is the ordinary least square method which, despite the simplicity of calculating and providing the BLUE estimator of parameters, in some situations leads to misleading solutions. For example, we can mention the problems of multi-collinearity and outliers in the data set. The least trimmed squares method which is one of the most popular of robust regression methods decreases the influence of outliers as much as possible. The main goal of this paper is to provide a robust ridge estimation in order to model dental age data. Among the methods used to determine age, the most popular method throughout the world is the modern modified Demirjian method that is based on the calcification of the permanent tooth in panoramic radiography. It has been shown that using the robust ridge estimator is leading to reduce the mean squared error in comparison with the OLS method. Also, the proposed estimators were evaluated in simulated data sets.
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