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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
A Simulation-Based Study Of Location-Shift Models Under Non-Normal Conditions, Ummay Khayrunnesa Anika
A Simulation-Based Study Of Location-Shift Models Under Non-Normal Conditions, Ummay Khayrunnesa Anika
Theses and Dissertations
In this study, we compare ordinary least squares (OLS), generalized least squares (GLS), M- and quantile regression (QR) estimators for a continuous response variable under different scenarios by conducting a simulation study. We assess the performance of the estimators in terms of bias, average distance, mean squared error, coverage probability, and ratio of estimated standard error and empirical standard deviation. OLS estimator performs the best when the errors are homoscedastic normal or homoscedastic but skewed (exponential) having no outliers. GLS estimator shows good comparative results to QR when the errors are heteroscedastic normal or heteroscedastic heavy-tailed (t-distributed). The most satisfactory …
Comparing Various Robust Estimation Techniques In Regression Analysis, Tracy S. Morrison
Comparing Various Robust Estimation Techniques In Regression Analysis, Tracy S. Morrison
All Graduate Theses, Dissertations, and Other Capstone Projects
In regression analysis, the use of the ordinary least squares (OLS) method is inadvisable when dealing with outlier or extreme observations. As a result, we require a method of robust estimation in which the estimation value is not significantly affected by outlier or extreme observations. Four methods of estimation will be compared in this paper in order to determine the best estimation: the M estimation method, the Least Trimmed Square Estimator, the S-estimation method, and the MM estimation method in robust regression. We discover that the best method is the MM-estimation method in this study. The M-estimation method is an …
Robust Heteroscedasticity Consistent Covariance Matrix Estimator Based On Robust Mahalanobis Distance And Diagnostic Robust Generalized Potential Weighting Methods In Linear Regression, M. Habshah, Muhammad Sani, Jayanthi Arasan
Robust Heteroscedasticity Consistent Covariance Matrix Estimator Based On Robust Mahalanobis Distance And Diagnostic Robust Generalized Potential Weighting Methods In Linear Regression, M. Habshah, Muhammad Sani, Jayanthi Arasan
Journal of Modern Applied Statistical Methods
The violation of the assumption of homoscedasticity and the presence of high leverage points (HLPs) are common in the use of regression models. The weighted least squares can provide the solution to heteroscedastic regression model if the heteroscedastic error structures are known. Based on Furno (1996), two robust weighting methods are proposed based on HLP detection measures (robust Mahalanobis distance based on minimum volume ellipsoid and diagnostic robust generalized potential based on index set equality (DRGP(ISE)) on robust heteroscedasticity consistent covariance matrix estimators. Results obtained from a simulation study and real data sets indicated the DRGP(ISE) method is superior.
Robust Inference For Regression With Spatially Correlated Errors, Juchi Ou, Jeffrey M. Albert
Robust Inference For Regression With Spatially Correlated Errors, Juchi Ou, Jeffrey M. Albert
Journal of Modern Applied Statistical Methods
A robust variance estimator for a regression model with spatially correlated errors is proposed using the estimated empirical covariogram. Simulations studies show unbiasedness and robustness for the OLS but not for the GLS estimates. The new robust variance estimation method is applied to hospital quality data.. Stephanie A.
A Monte Carlo Comparison Of Regression Estimators When The Error Distribution Is Long-Tailed Symmetric, Oya Can Mutan, Birdal Şenoğlu
A Monte Carlo Comparison Of Regression Estimators When The Error Distribution Is Long-Tailed Symmetric, Oya Can Mutan, Birdal Şenoğlu
Journal of Modern Applied Statistical Methods
The performances of the ordinary least squares (OLS), modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares (WIN), trimmed least squares (TLS), Theil’s (Theil) and weighted Theil’s (Weighted Theil) estimators are compared under the simple linear regression model in terms of their bias and efficiency when the distribution of error terms is long-tailed symmetric.
Email: A Note On Hypothesis Tests After Correction For Autocorrelation: Solace For The Cochrane-Orcutt Method?, Terry E. Dielman
Email: A Note On Hypothesis Tests After Correction For Autocorrelation: Solace For The Cochrane-Orcutt Method?, Terry E. Dielman
Journal of Modern Applied Statistical Methods
The behavior of the t test in small samples for coefficient significance in time-series regressions is examined after using the Prais-Winsten (PW) and Cochrane-Orcutt (CO) corrections for autocorrelation. Results are compared to ordinary least squares and generalized least squares.
Variance Estimation In Time Series Regression Models, Samir Safi
Variance Estimation In Time Series Regression Models, Samir Safi
Journal of Modern Applied Statistical Methods
The effect of variance estimation of regression coefficients when disturbances are serially correlated in time series regression models is studied. Variance estimation enters into confidence interval estimation, hypotheses testing, spectrum estimation, and expressions for the estimated standard error of prediction. Using computer simulations, the robustness of various estimators, including Estimated Generalized Least Squares (EGLS) was considered. The estimates of variance of the coefficient estimators produced by computer packages were considered. Models were generated with a second order auto-correlated error structure, considering the robustness of estimators based upon misspecified order. Ordinary Least Squares (OLS) (order zero) estimates outperformed first order EGLS. …
The Efficiency Of Ols In The Presence Of Auto-Correlated Disturbances In Regression Models, Samir Safi, Alexander White
The Efficiency Of Ols In The Presence Of Auto-Correlated Disturbances In Regression Models, Samir Safi, Alexander White
Journal of Modern Applied Statistical Methods
The ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance, and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are correlated. Using computer simulations, the robustness of various estimators are considered, including estimated generalized least squares. It was found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, rules of thumb are presented to guide practitioners in the choice …