Nonparametric Bootstrap Estimation of Confidence Interval in Base Station Test
In most test analysis,we always want to construct a parameter model, and then through sample statistics and model parameters,we can get the properties,or characteristics of what we concern about.Confidence intervals can give an estimate of the range within which the true value of the statistic lies.And a narrow confidence interval indicates the low variability of the statistic,which can give a strong support for the conclusion made from the statistical analysis.In base station test,we can barely construct an accurate parameter model,because the measured value varies with many factors.Without theoretical formulas,we can not get accurate assessment of the measured value.The Efron bootstrap statistical analysis can just solve this problem.In this article,we introduce several nonparametric bootstrap methods in assessing the accuracy of s~mple statist/e, and the validatious of these methods are performed with both Measured data and simulation data.We have found that all of those methods give an accurate prediction of the 90 percent confidence interval for the mean.And they can still work even under small data sets.
Nonparametric bootstrap base station test confidence intervals mean
Dalin ma Yougang Gao Dan Shi Yuanmao Shen Yaozhong Zhou Qinghai Yang
Beijing University of Posts and Telecommunications,P.R.China China Railway Engineering Consultants Group,P.R.China
国际会议
Asia-Pacific Conference on Environmental Electromagnetics CEEM2009(第五届亚太环境电磁学学术会议)
西安
英文
390-394
2009-09-16（万方平台首次上网日期，不代表论文的发表时间）