|
|
|
 |
Search published articles |
 |
|
Showing 8 results for Outlier
Miss Azade Ghazanfari Hesari, ,
Volume 20, Issue 2 (10-2015)
Abstract
One of the most important problem in any statistical analysis is the existence of unexpected observations. Some
observations are not a part of the study and are known as outliers. Studies have shown that the outliers affect to the
performance of statistical standard methods in models and predictions. The point of this work is to provide a couple
of statistical package in R software to identify outliers in circular-circular regression which is written by the author,
we introduce a brief explanation about the circular data and circular regression, then the packages in R for circular
regression introduced. After wand, the functions in the package CircOutlier will be described.
, , ,
Volume 22, Issue 2 (3-2018)
Abstract
Robust regression is an appropriate alternative for ordinal regression when outliers exist in a given data set. If we have fuzzy observations, using ordinal regression methods can't model them; In this case, using fuzzy regression is a good method. When observations are fuzzy and there are outliers in the data sets, using robust fuzzy regression methods are appropriate alternatives. In this paper, we propose a fuzzy least square regression analysis. When independent variables are crisp, the dependent variable is fuzzy number and outliers are present in the data set. In the proposed method, the residuals are ranked as the comparison of fuzzy sets. In the proposed method, the residuals are ranked as the comparison of fuzzy sets, and the weight matrix is defined by the membership function of the residuals. Weighted fuzzy least squares estimators (WFLSE) are obtained by using weight matrix. Two examples are discussed and results of these examples are presented. Finally, we compare this proposed method with ordinal least squares method using the goodness of fit indices.
Ms Sara Jazan, Dr Seyyed Morteza Amini,
Volume 22, Issue 2 (3-2018)
Abstract
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity, the large number of regressor variables with respect to sample size, specially in high dimensional sparse models, are problems which result in efficiency reduction of inferences in classical regression methods. In this paper, we first study the disadvantages of classical least squares regression method, when facing with outliers, multicollinearity and sparse models. Then, we introduce and study robust and penalized regression methods, as a solution to overcome these problems. Furthermore, considering outliers and multicollinearity or sparse models, simultaneously, we study penalized-robust regression methods. We examine the performance of different estimators introdused in this paper, through three different simulation studies. A real data set is also analyzed using the proposed methods.
Dr. Mousa Golalizadeh, Mr. Amir Razaghi,
Volume 24, Issue 1 (9-2019)
Abstract
The Principal Components Analysis is one of the popular exploratory approaches to reduce the dimension and to describe the main source of variation among data. Despite many benefits, it is encountered with some problems in multivariate analysis. Having outliers among data significantly influences the results of this method and it sounds a robust version of PCA is beneficial in this case. In addition, having moderate loadings in the final results makes the interpretation of principal components rather difficult. One can consider a version of sparse components in this case. We study a hybrid approach consisting of joint robust and sparse components and conduct some simulations to evaluate and compare it with other traditional methods. The proposed technique is implemented in a real-life example dealing with the crime rate in the USA.
, ,
Volume 24, Issue 2 (3-2020)
Abstract
The minimum density power divergence method provides a robust estimate in the face of a situation where the dataset includes a number of outlier data.
In this study, we introduce and use a robust minimum density power divergence estimator to estimate the parameters of the linear regression model and then with some numerical examples of linear regression model, we show the robustness of this estimator in the face of a dataset which includes a number of outliers.
Ms Monireh Maanavi, Dr Mahdi Roozbeh,
Volume 26, Issue 1 (12-2021)
Abstract
The method of least squares is a very simple, practical and useful approach for estimating regression coefficients of the linear models. This statistical method is used by users of different fields to provide the best unbiased linear estimator with the least variance. Unfortunately, this method will not have reliable output if outliers are present in the dataset, as the collapse point (estimator consistency criterion) of this method is 0% . It is therefore important to identify these observations. Until now, the various methods have been proposed to identify these observations. In this article, the proposed methods are reviewed and discussed in details. Finally, by presenting a simulation example, we examine each of the proposed methods.
Dr Mahdi Roozbeh, Ms mlihe Malekjafarian, Ms Monireh Maanavi,
Volume 26, Issue 2 (3-2022)
Abstract
The most important goal of statistical science is to analyze the real data of the world around us. If this information is analyzed accurately and correctly, the results will help us in many important decisions. Among the real data around us which its analysis is very important, is the water consumption data. Considering that Iran is located in a semi-arid climate area of the earth, it is necessary to take big steps for predicting and selecting the best and the most appropriate accurate models of water consumption, which is necessary for the macro-national decisions. But analyzing the real data is usually complicated. In the analysis of the real data set, we usually encounter with the problems of multicollinearity and outliers points. Robust methods are used for analyzing the datasets with outliers and ridge method is used for analyzing the data sets with multicollinearity. Also, the restriction on the models is resulted from using non-sample information in estimation of regression coefficients. In this paper, it is proceeded to model the water consumption data using robust stochastic restricted ridge approach and then, the performance of the proposed method is examined through a Monte Carlo simulation study.
Seyyed Roohollah Roozegar, Amir Reza Mahmoodi,
Volume 27, Issue 2 (3-2023)
Abstract
Many regression estimation techniques are strongly affected by outlier data and many errors occur in their estimation.
In the recent years, robust methods have been developed to solve this issue. The minimum density power divergence
estimator is an estimation method based on the minimum distance between two density functions, which provides a
robust estimate in situations where the data contain a number of outliers. In this research, we present the robust estimation
method of minimum density power divergence to estimate the parameters of the Poisson regression model,
which can produce robust estimators with the least loss in efficiency. Also, we will investigate the performance of the
proposed estimators by providing a real example.
|
|
