Bootstrap confidence intervals for industrial recurrent event data

Pesquisa Operacional

Osvaldo Anacleto²  Francisco Louzada¹ 

[1] ,The Open University Department of Mathematics and Statistics Milton Keynes,United Kingdom

关键词: industrial data;    recurrent events;    bootstrap;    asymptotic theory;    confidence intervals;   

DOI  :  10.1590/S0101-74382012005000008

来源: SciELO

【 摘 要 】

Industrial recurrent event data where an event of interest can be observed more than once in a single sample unit are presented in several areas, such as engineering, manufacturing and industrial reliability. Such type of data provide information about the number of events, time to their occurrence and also their costs. Nelson (1995) presents a methodology to obtain asymptotic confidence intervals for the cost and the number of cumulative recurrent events. Although this is a standard procedure, it can not perform well in some situations, in particular when the sample size available is small. In this context, computer-intensive methods such as bootstrap can be used to construct confidence intervals. In this paper, we propose a technique based on the bootstrap method to have interval estimates for the cost and the number of cumulative events. One of the advantages of the proposed methodology is the possibility for its application in several areas and its easy computational implementation. In addition, it can be a better alternative than asymptotic-based methods to calculate confidence intervals, according to some Monte Carlo simulations. An example from the engineering area illustrates the methodology.

【 授权许可】

CC BY   
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