An algorithm for robust linear estimation with grouped data

Rivero, Carlos; Valdés, Teófilo

doi:10.1016/j.csda.2008.07.009

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Yang and Yu (2005) proposed a method to estimate the parameters in fuzzy class models using a fuzzy clustering algorithm. Recently, many techniques have been studied concerning grouped data, e.g., Bassetti et al (2007), Meister (2007), Rivero and Valdes (2008), Lambert (2011) and Ryan et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm

Zhang

Xie

et al. 2014

Computational Statistics & Data Analysis

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The purpose of this article is to derive a lower confidence limit for reliability given a grouped data set. This is done by using a quantile filling algorithm which generates pseudo failure data from grouped data. A general framework of this approach is first introduced. The cases for the exponential distribution, Weibull distribution, and lognormal distribution are used to illustrate this approach. Simulation studies are carried out and the results show that it is a useful method handling grouped data. Two field lifetime data sets are also analyzed to demonstrate its feasibility. Some further improvements are discussed as well.

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Section: Introductionmentioning

confidence: 99%

Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm

Zhang

Xie

et al. 2014

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

show abstract

“…Interval-valued data have been used to collect single-valued data (see for example Diday and Émilion, 1998). In recent years, big data and grouped data have been shown using interval-valued data (see for example Calle and Gómez, 2001;Rivero and Valdes, 2008;Trutschnig et al, 2009;Huber et al, 2009;Billard, 2011).…”

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confidence: 99%