文章基本信息

标题：Multiple Crossing Sequential Fixed-Size Confidence Regions for Regression Parameters Under Normality
本地全文：下载
作者：Sankha Muthu Poruthotage ; Nitis Mukhopadhyay
期刊名称：Sri Lankan Journal of Applied Statistics
印刷版ISSN：1391-4987
电子版ISSN：2424-6271
出版年度：2014
卷号：5
期号：4
页码：147-182
DOI：10.4038/sljastats.v5i4.7789
语种：English
出版社：The Institute of Applied Statistics, Sri Lanka
摘要：The purely sequential sampling procedure proposed by Mukhopadhyay and Abid (1986) is customarily used to construct a fixed-size confidence region for regression parameters. This methodology has asymptotic efficiency and asymptotic consistency properties, but it does not have the exact consistency property. We propose that sequential sampling be continued allowing the sample size to cross a corresponding boundary multiple times. The asymptotic efficiency and asymptotic consistency properties are ascertained for multiple crossing stopping rules (Theorem 2.1). A truncation technique as well as a fine-tuning adjustment are developed. The simulated data are generated by realistic models arising from a study that investigates the association between prostate-specific antigen (PSA) and a number of appropriate prognostic clinical covariates. We highlight via large-scale simulations the remarkable gain in nearly achieving the target coverage without significant over-sampling.DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7789
关键词：Applied Statistics; Statistics;Boundary crossing; Consistency; Efficiency; Fine-tuning; Prostatespecific antigen; Second-order efficiency; Simulations; Truncation