不同类型排序集抽样下skew-normal分布的Fisher信息量

Fisher Information in Different Types of Ranked Set Sampling from Skew-Normal Distribution

Fisher信息量是统计推断的基本概念,在统计分析的许多领域中都发挥着重要作用.文章分别在简单随机抽样,排序集抽样(RSS),极值RSS (ERSS)和中位数RSS (MRSS)下研究了skew-normal分布中对应样本所含偏度参数的Fisher信息量.数值结果显示,同等样本容量不同类型完美排序集样本比简单随机样本提供更多的信息.数值结果也显示所有信息都会因排序出错而减少.

Fisher information is a fundamental concept of statistical inference and plays an important role in many areas of statistical analysis.In this article,Fisher information in the corresponding samples about the skewness parameter from skewnormal distribution under simple random sampling,ranked set sampling(RSS),extreme RSS(ERSS) and median RSS(MRSS) will be respectively studied.The numerical results show that different types of perfect ranked set sample carry more information than a simple random sample of equivalent size.The numerical results also show all information will decrease as rank error.