Robust regression and outlier detection
Robust regression and outlier detection
A clustering algorithm for identifying multiple outliers in linear regression
Computational Statistics & Data Analysis
R and S-Plus Companion to Applied Regression
R and S-Plus Companion to Applied Regression
Construction and operation of a knowledge base on intelligent machine tools
WSEAS TRANSACTIONS on SYSTEMS
An evaluation of test statistics for detecting level change in BL(1,1,1,1) models
WSEAS Transactions on Mathematics
Multiple linear regression in forecasting the number of asthmatics
WSEAS Transactions on Information Science and Applications
Estimating bias and RMSE of indirect effects using rescaled residual bootstrap in mediation analysis
WSEAS Transactions on Mathematics
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In this paper we propose a new Weighted Bootstrap with Probability (WBP). The basic idea of the proposed bootstrap technique is to do re-sampling with probabilities. These probabilities become the control mechanism for getting good estimates when the original data set contain multiple outliers. Numerical examples and simulation study are carried out to evaluate the performance of the WBP estimates as compared to the Bootstrap 1 and Diagnostic-Before Bootstrap estimates. The results of the study signify that the WBP method is more efficient than the other two methods.