Tests for successive differences of quantiles

Soni, Pooja; Dewan, Isha; Jain, Kanchan

doi:10.1016/j.spl.2014.10.013

Cited by 4 publications

(3 citation statements)

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“…in terms of the quantile function. Sankaran, Nair, and Sreedevi (2010) derived a test procedure for comparing various risks using sub-quantile functions and Soni, Dewan, and Jain (2015) proposed tests for successive comparison of quantiles using the quantile functions. Soni, Dewan, and Jain (2012) developed a non-parametric estimator of the quantile density function.…”

Section: Introductionmentioning

confidence: 99%

The Cause Specific Hazard Quantile Function

Paduthol

Dewan

2018

AJS

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In this paper, we discuss modeling and analysis of competing risks data using the quantile function. We introduce and study the cause specific hazard quantile function. We present competing risks models using various functional forms for the cause specific hazard quantile functions. A non-parametric estimator of the cause specific hazard quantile function is derived. Asymptotic properties of the estimator are studied. Simulation studies are carried out to assess the performance of the estimator. Finally, we apply the proposed procedure to real life data sets.

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Section: Introductionmentioning

confidence: 99%

The Cause Specific Hazard Quantile Function

Paduthol

Dewan

2018

AJS

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“…Statistical analysis of data based 15 on quantile functions has been carried out in reliability and survival analysis and other branches of applied statistics (see, for example, [37], [44], [48], [30], [31], [39] and [41]). [46,47] have developed multiple comparison procedures for quantile functions. Nonparametric test procedures under competing risks have been developed by [34], [22] and [40].…”

Section: Introductionmentioning

confidence: 99%

Nonparametric estimation of a quantile density function by wavelet methods

Chesneau

Dewan

Doosti

2016

Computational Statistics & Data Analysis

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Please cite this article as: Chesneau, C., Dewan, I., Doosti, H., Nonparametric estimation of a quantile density function by wavelet methods. Computational Statistics and Data Analysis (2015), http://dx. AbstractIn this paper nonparametric wavelet estimators of the quantile density function are proposed.Consistency of the wavelet estimators is established under the L p risk. A simulation study illustrates the good performance of our estimators.

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“…In such situations, comparison of mean has no relevance but comparing quantiles becomes important (Ref. Kotz and Seier 2009;Nair et al 2013;Soni et al 2015). There are situations where it is of interest to compare quantiles other than the median.…”

Section: Introductionmentioning

confidence: 99%