Self-service capacity expansion method based on correlation coefficient criterion

Abstract

The invention discloses a self-service capacity expansion method based on a correlation coefficient criterion. The method comprises the following steps: S1, initializing parameters, and setting a correlation coefficient threshold value rho epsilon, a positive integer M, a self-service sample capacity B and an equal fraction m of a histogram function; S2, copying a new sample, generating a positiveinteger R-U (0, M) obeying uniform distribution randomly, calculating a remainder p = mod (R, n), wherein n is the sample capacity; S3, calculating the mean value of the copied samples, if the ith new sample is the ith new sample, repeating S2 to obtain a group of copied samples, calculating the mean value of the copied samples; S4, obtaining self-service resampling samples, and repeating the steps S2 and S3 to obtain the similarity rho (f (x))) of the self-service resampling samples to calculate the resampling samples and the original samples; f (x *)) if rho (f (x); and if f (x *) is greater than or equal to rho epsilon, outputting a resampling sample, otherwise, repeating the steps S1 to S4 until a similarity condition is satisfied. The resampling sample obtained by the method fully utilizes the information of the given sample, and the obtained resampling sample is closer to the real situation.

Description

technical field [0001] The invention belongs to the field of probability mathematical statistics, and in particular relates to a self-service expansion method based on a correlation coefficient criterion. Background technique [0002] In engineering practice, a common method to solve small samples is to increase the original sample size by resampling based on the theory of probability, mathematics and statistics. Among them, Professor Efron, B of Stanford University in the United States proposed a new statistical analysis method in 1977 - the self-help method (Bootstrap method). Prior to this, Quenodille, M.H proposed Jacknife estimation of estimator bias and Tukey, J.W proposed Jacknife estimation of estimator variance. Since then, the BayesianBootstrap method (also known as random weighting method) has appeared. [0003] Through the analysis, it is found that the bootstrap method mainly uses random methods to copy samples from the original sample, so the bootstrap sample...

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Especially when the real distribution is a continuous distribution, it is difficult to obtain the parameter estimation o

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