Bootstrap methods for median regression models

Article Abstract:

The problems associated with using the least-absolute deviation (LAD) estimator for a median-regression model ran through a boostrap procedure was surmounted with the development of a smoothed LAD estimator that satisfy standard conditions for obtaining asymptotic refinements through the bootstrap procedure. The smoothed LAD smooths the cusps in the solution of the standard LAD by replacing the indicator function with a smooth function, making the smoothed LAD asymptotically equivalent to the standard LAD.

Research and Development in the Physical, Engineering, and Life Sciences, Economics, Research and Development in the Social Sciences and Humanities, Statistics, Models, Economic research, Statistics (Mathematics), Statistical hypothesis testing, Approximation theory, Approximation

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Bootstrap critical values for tests based on generalized-method-of-moments estimators

Article Abstract:

Monte Carlo experiments are performed to evaluate the feasibility of using bootstrap critical values as generalized-method-of-moments estimators. The values' ability to handle dependent data and identify restrictions are considered in the analysis. Results show that the use of the bootstrap critical values reduces the errors in level that occur when critical values based on the first-order asymptotic theory are employed.

Methods, Monte Carlo method, Monte Carlo methods, Parameter estimation

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Identification and robustness with contaminated and corrupted data

Article Abstract:

Finite bounds may be placed on unidentified population parameters found in error models derived under assumptions of robust estimation. The latter, which seeks to obtain point estimators which remain unaffected by data error, can thus be replaced by bound estimation whenever the sampled data is contaminated or corrupted. This is because the bounds provide information that is otherwise not given by robust estimation.

Statistical sampling, Estimation theory, Sampling (Statistics)

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