[1] Altman, N, Leger, C (1995) Bandwidth selection for kernel distribution function estimation. J Stat PlanInference, 46, 195-214.
[2] Chen, SX (1999) Beta kernel estimators for density functions. Comput Stat Data Anal, 31, 131-145.[3] Chen, SX (2000) Probability density function estimation using gamma kernels. Ann Inst Stat Math,52, 471-480.
[4] Du P, Tang L (2009) Transformation-invariant and nonparametric monotone smooth estimation ofROC curves. Stat Med, 28, 349-359.
[5] Duong, T. (2016). Non-parametric smoothed estimation of multivariate cumulative distribution an survival functions, and receiver operating characteristic curves. J Korean Stat Soc, 45 (1), 33-50.
[6] Fawcett T (2006) An introduction to ROC analysis. Pattern Recognition Lett, 27, 861-874. Green DM,Swets JA (1966) Signal detection theory and psychophysics. Wiley New York.
[7] Green DM, Swets JA (1966) Signal detection theory and psychophysics. Wiley New York.
[8] Hans P, Albert A, Born J, Chapelle JP (1985) Derivation of a bioclinical prognostic index in severehead injury. Intensive Care Med, 11, 186-191.
[9] Horová I, Koláček J, Zelinka J, El-Shaarawi AH (2008) Smooth estimates of distribution functionswith application in environmental studies. Article in Proceedings. Advanced topics on mathematicalbiology and ecology, 1, 122-127.
[10] Hsieh F, Turnbull BW (1996) Nonparametric and semiparametric estimation of the receiver operatingcharacteristic curve. Ann. Stat., 24, 25-40.
[11] Koláček J, Karunamuni RJ (2009) On boundary correction in kernel estimation of ROC curves. AustrianJ. Stat., 38, 17–32-17–32.
[12] Lafaye de Micheaux, P., & Ouimet, F. (2021). A study of seven asymmetric kernels for the estimationof cumulative distribution functions. Mathematics, 9 (20), 2605.
[13] Lasko TA. Bhagwat JG, Zou KH, Ohno-Machado L (2005) The use of receiver operating characteristiccurves in biomedical informatics. J. Biomed. Inform., 38, 404-415.
[14] Lloyd CJ (1998) Using smoothed receiver operating characteristic curves to summarize and comparediagnostic systems. J Am Stat Assoc, 93, 1356-1364.
[15] Lloyd CJ, Yong Z (1999) Kernel estimators of the ROC curve are better than empirical. Stat ProbabLett, 44, 221-228.
[16] Marchant C, Bertin K, Leiva V, Saulo H (2013) Generalized Birnbaum–Saunders kernel density
estimators and an analysis of financial data. Comput Stat Data Anal, 63, 1-15.
[17] Mansouri, B., Atiyah Sayyid Al-Farttosi, S., Mombeni, H., & Chinipardaz, R. (2022). Estimating
Cumulative Distribution Function Using Gamma Kernel. J. Sci. Islam. Repub. 33 (1), 45-54.
[18] Mombeni HA, Mansouri B, Akhoond MR (2021) Asymmetric kernels for boundary modification indis-tribution function estimation. Revstat Stat. J.
[19] Pulit M (2016) A new method of kernel-smoothing estimation of the ROC curve. Metrika, 79, 603-634.
[20] Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall: London.[21] Tang L, Du P, Wu C (2010) Compare diagnostic tests using transformation-invariant smoothed ROCcurves. J Stat Plan Inference, 140, 3540-3551.
[22] Tenreiro C (2013) Boundary kernels for distribution function estimation. Revstat Stat. J., 11, 169-190.
[23] Tenreiro C (2018) A new class of boundary kernels for distribution function estimation. Commun.Stat. Theory Methods, 47, 5319-5332.
[24] Wasserman L (2006) All of Nonparametric Statistics, Springer: New York.
[25] Zhang S, Karunamuni RJ, Jones MC (1999) An improved estimator of the density function at theboundary. J Am Stat Assoc, 94, 1231-1240.
[26] Zhou XH,. McClish DK, Obuchowski NA (2009) Statistical Methods in Diagnostic Medicine. JohnWiley & Sons.
[27] Zou KH. Hall W, Shapiro DE (1997) Smooth non-parametric receiver operating characteristic (ROC)curves for continuous diagnostic tests. Stat Med, 16, 2143-2156.