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                                       Details for article 14 of 19 found articles
 
 
  Pivotal Quantities Based on Sequential Data: A Bootstrap Approach
 
 
Title: Pivotal Quantities Based on Sequential Data: A Bootstrap Approach
Author: Saavedra, Pedro
Santana, Angelo
Quintana, Maria Del Pino
Appeared in: Communications in statistics
Paging: Volume 35 (2006) nr. 4 pages 1005-1018
Year: 2006-12-01
Contents: 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.
Publisher: Taylor & Francis
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 14 of 19 found articles
 
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