Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42851
Title: Pivotal quantities based on sequential data: A bootstrap approach
Authors: Saavedra, Pedro
Santana, Angelo 
Del Pino Quintana, María 
UNESCO Clasification: 120905 Análisis y diseño de experimentos
120906 Métodos de distribución libre y no paramétrica
120913 Técnicas de inferencia estadística
120903 Análisis de datos
240401 Bioestadística
Keywords: Bootstrap
Sequential designs
Wang and Tsiatis tests
Issue Date: 2006
Journal: Communications in Statistics Part B: Simulation and Computation 
Abstract: A bootstrap algorithm is provided for obtaining a confidence interval for the mean of a probability distribution when sequential data are considered. For this kind of data the empirical distribution can be biased but its bias is bounded by the coefficient of variation of the stopping rule associated with the sequential procedure. When using this distribution for resampling the validity of the bootstrap approach is established by means of a series expansion of the corresponding pivotal quantity. A simulation study is carried out using Wang and Tsiatis type tests and considering the normal and exponential distributions to generate the data. This study confirms that for moderate coefficients of variation of the stopping rule, the bootstrap method allows adequate confidence intervals for the parameters to be obtained, whichever is the distribution of data.
URI: http://hdl.handle.net/10553/42851
ISSN: 0361-0918
DOI: 10.1080/03610910600880526
Source: Communications In Statistics-Simulation And Computation[ISSN 0361-0918],v. 35 (4), p. 1005-1018
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