13 October 2008 Maximum entropy bootstrap method for parameter estimation
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Abstract
The maximum entropy bootstrap method is proposed to resolve the problem about parameter estimation under the condition that the number of test times is small while every time the number of test data is much, in test analysis for stochastic processes with unknown probability distribution. First the maximum entropy distribution of each test data is established, then information about the maximum entropy distributions of all tests is fused by bootstrap resampling, and lastly the inference of attribute of population is drawn from identifying the parameters of every individual, describing the stable state of population. Simulation experiment shows that the estimated results using the maximum entropy bootstrap method are coincident with the real truth of the subject investigated, with the relative errors of 0.14%~2.39% and the better results by a 9%~20% decrease of the relative errors as compared with the maximum entropy method and the bootstrap method.
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Lin Zhang, Lin Zhang, Xintao Xia, Xintao Xia, Zhongyu Wang, Zhongyu Wang, } "Maximum entropy bootstrap method for parameter estimation", Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 71280A (13 October 2008); doi: 10.1117/12.806437; https://doi.org/10.1117/12.806437
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