si、Hui-LiuR-, HU Thei-wen LIU Hai-tao. An Improvement to Interval Estimation for Small Samples[J]. 2017, 22(1): 109-113.DOI: 10.15938/j.jhust.2017.01.019.
Because it is difficult and complex to determine the probability distribution of small samples,it isimproper to use traditional probability theory to process parameter estimation for small samples. Bayes Bootstrapmethod is always used in the project. Although,the Bayes Bootstrap method has its own limitation,In this articlean improvement is given to the Bayes Bootstrap method,This method extended the amount of samples by numericalsimulation without changing the circumstances in a small sample of the original sample. And the new method cangive the accurate interval estimation for the small samples. Finally,by using the Monte Carlo simulation to modelsimulation to the specific small sample problems. The effectiveness and practicability of the Improved-Bootstrapmethod was proved.