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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Level Robust Methods Based On The Least Squares Regression Estimator, Marie Ng, Rand R. Wilcox
Level Robust Methods Based On The Least Squares Regression Estimator, Marie Ng, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Heteroscedastic consistent covariance matrix (HCCM) estimators provide ways for testing hypotheses about regression coefficients under heteroscedasticity. Recent studies have found that methods combining the HCCM-based test statistic with the wild bootstrap consistently perform better than non-bootstrap HCCM-based methods (Davidson & Flachaire, 2008; Flachaire, 2005; Godfrey, 2006). This finding is more closely examined by considering a broader range of situations which were not included in any of the previous studies. In addition, the latest version of HCCM, HC5 (Cribari-Neto, et al., 2007), is evaluated.
The Bootstrap Method For The Selection Of A Shrinkage Factor In Two-Stage Estimation Of The Reliability Function Of An Exponential Distribution, Makarand V. Ratnaparkhi, Vasant B. Waikar, Fredrick J. Schuurmann
The Bootstrap Method For The Selection Of A Shrinkage Factor In Two-Stage Estimation Of The Reliability Function Of An Exponential Distribution, Makarand V. Ratnaparkhi, Vasant B. Waikar, Fredrick J. Schuurmann
Journal of Modern Applied Statistical Methods
An application of a bootstrap method for selecting a suitable shrinkage factor for the two-stage shrinkage estimator of a reliability function for the exponential distribution is discussed. The estimator obtained here has higher efficiency as compared to the one where the shrinkage factor is not subjected to bootstrapping.