Estimating the inverse distribution function at the boundary
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 15 (2022) no. 4, pp. 510-522.

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Most of existing quantile estimators have problems of inefficiency in extreme quantiles. To solve this problem, in this paper we suggested an alternative estimator and provided its asymptotic behaviour when quantile near the boundary value. A simulation studies and two real data applications were included to demonstrate the efficiency and reliability of our theoretical results.
Keywords: mean square error, optimal bandwidth, boundary quantiles.
Mots-clés : kernel quantile estimation
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Nassima Almi; Abdallah Sayah. Estimating the inverse distribution function at the boundary. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 15 (2022) no. 4, pp. 510-522. http://geodesic.mathdoc.fr/item/JSFU_2022_15_4_a9/

[1] M.Denuit, J. Dhaene, Actuarial Theory for Dependent Risk: Measures, Orders and Models, Wiley, New York, 2005

[2] H.Yamato, “Uniform convergence of an estimator of a distribution function”, Bull.Math.Statist., 15 (1973), 69–78

[3] R.D.Reiss, “Nonparametric estimation of smooth distribution functions”, Scand. J.Statist., 8 (1981), 116–119

[4] M.Falk, “Relative efficiency and deficiency of kernel type estimators of smooth distribution functions”, Statist. Neerlandica, 37 (1983), 73–83

[5] M.Rosenblatt, “Remarks on Some Nonparametric Estimates of a Density Function”, The Annals of Mathematical Statistics, 27 (1956), 832–837

[6] E.Parzen, “Nonparametric Statistical Data Modelling”, Journal of the American Statistical Association, 74 (1979), 105–131

[7] E.A.Nadaraya, “Some new estimates for distribution function”, Theory of Probab., 9 (1964), 497–500

[8] N.Almi, A.Sayah, “nonparametric kernel distribution function estimation near endpoints”, Advances in Mathematics: Scientific Journal, 10 (2021), 3679–3697 | DOI

[9] M.Tour, A.Sayah, Y.Djebrane, “A Modified Champernowne Transformation to Improve Boundary Effect in Kernel Distribution Estimation”, Afrika Statistika, 12 (2017), 1219–1233 | DOI

[10] C.Tenreiro, “Boundary Kernels for Distribution Function Estimation”, REVSTAT Statistical Journal, 11 (2013), 169–190

[11] C.Tenreiro, A note on boundary kernels for distribution function estimation, 2015, arXiv: 1501.04206

[12] J.Kolácek, R.J.Karunamuni, “On boundary correction in kernel estimation of ROC curves”, Austrian Journal of Statistics, 38 (2009), 17–32 | DOI

[13] S.Zhang, L.Zhong, Z.Zhang, “Estimating a Distribution Function at the Boundary”, Austrian Journal of Statistics, 49 (2020), 1–23 | DOI

[14] J.Galambos, The asymptotic theory of extreme order statistics, Krieger, Malabar, Florida, 1978

[15] H.A.David, Order Statistics, 2nd Edition, John Wiley, New York, 1981

[16] S.S.Ralescu, S.Sun, “Necessary and sufficient conditions for the asymptotic normality of perturbed sample quantiles”, J. Statist. Plann. Inference, 35 (1993), 55–64

[17] A.Azzalini, “A note on the estimation of a distribution function and quantiles by a kernel method”, Biometrika, 68 (1981), 326–328

[18] S.S.Yang, “A Smooth Nonparametric Estimator of a Quantile Function”, Journal of the American Statistical Association, 80 (1985), 1004–1011

[19] S.J.Sheater, J.S.Marron, “Kernel quatile estimtors”, Journal of the American Statistical Associatio, 85 (1990), 410–416

[20] F.E.Harrell, C.E.Davis, “A New Distribution-Free Quantile Estimator”, Biometrika, 69 (1982), 635–640

[21] C.Park, Smooth nonparametric estimation of a quantile function under right censoring using beta kernels, Technical Report (TR 2006-01-CP), Department of Mathematical Sciences, Clemson University, 2006

[22] A.Charpentier, A.Oulidi, “Beta kernel quantile estimators of heavy-tailed loss distributions”, Stat. Comput., 20 (2010), 35–55 | DOI

[23] A.Sayah, Y.Djebrane, A Necir, “Champernowne transformation in kernel quantile estimation for heavy-tailed distributions”, Afrika Statistika, 5 (2010), 288–296

[24] M.D.Nichols, W.J.Padgett, “A bootstrap control chart for Weibull percentiles”, Qual Reliab Eng Int., 22 (2006), 141–151 | DOI

[25] Q.Bi, W.Gui, “Bayesian and classical estimation of stress-strength reliability for inverse Weibull lifetime models”, Algorithms, 10 (2017), 71 | DOI