A quantile based test for comparing cumulative incidence functions of competing risks models

Sankaran, P. G.; Nair, N. Unnikrishnan; Sreedevi, E. P.

doi:10.1016/j.spl.2010.01.023

Cited by 30 publications

(13 citation statements)

References 16 publications

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“…In the context of hypothesis testing, Klein (2007, 2008) as well as Sankaran et al (2010) have also studied Lin's resampling scheme to test for equality of different CIFs in extensive simulation studies. Spitoni et al (2012) investigated Lin's resampling method for estimating transition probabilities in semi-Markovian models with applications to survival analysis.…”

Section: Introductionmentioning

confidence: 99%

Bootstrapping Aalen-Johansen processes for competing risks: Handicaps, solutions, and limitations

Dobler¹,

Pauly²

2014

Electron. J. Statist.

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Statistical inference in competing risks models is often based on the famous Aalen-Johansen estimator. Since the corresponding limit process lacks independent increments, it is typically applied together with Lin's (1997) resampling technique involving standard normal multipliers. Recently, it has been seen that this approach can be interpreted as a wild bootstrap technique and that other multipliers, as e.g. centered Poissons, may lead to better finite sample performances, see Beyersmann et al. (2013). Since the latter is closely related to Efron's classical bootstrap, the question arises whether this or more general weighted bootstrap versions of Aalen-Johansen processes lead to valid results. Here we analyze their asymptotic behaviour and it turns out that such weighted bootstrap versions in general possess the wrong covariance structure in the limit. However, we explain that the weighted bootstrap can nevertheless be applied for specific null hypotheses of interest and also discuss its limitations for statistical inference. To this end, we introduce different consistent weighted bootstrap tests for the null hypothesis of stochastically ordered cumulative incidence functions and compare their finite sample performance in a simulation study.

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Section: Introductionmentioning

confidence: 99%

Bootstrapping Aalen-Johansen processes for competing risks: Handicaps, solutions, and limitations

Dobler¹,

Pauly²

2014

Electron. J. Statist.

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“…In many cases, QFs are more convenient as they are less influenced by extreme observations and thus provide a straightforward analysis with a limited amount of information. For a detailed and recent study on QF, its properties and its usefulness in the identification of models we refer to Lai and Xie (2006), Nair and Sankaran (2009), Nair et al (2011), Sankaran and Nair (2009), Sankaran et al (2010) and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Quantile based entropy function

Sunoj¹,

Sankaran²

2012

Statistics & Probability Letters

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Quantile functions are efficient and equivalent alternatives to distribution functions in modeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran, 2009). Motivated by this, in the present paper, we introduce a quantile based Shannon entropy function. We also introduce residual entropy function in the quantile setup and study its properties. Unlike the residual entropy function due to Ebrahimi (1996), the residual quantile entropy function determines the quantile density function uniquely through a simple relationship. The measure is used to define two nonparametric classes of distributions.

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“…For various properties of (2.3), one can refer to Nair et al (2008), Sankaran and Nair (2009), Nair and Vinesh Kumar (2010) and Sankaran et al (2010).…”

Section: Basic Resultsmentioning

confidence: 99%