Non parametric estimation of the conditional density function with right-censored and dependent data

References

• Aït-Sahalia, Y. 1999. Transition densities for interest rate and other non-linear diffusions. The Journal of Finance 54 (4):1361–95. doi:10.1111/0022-1082.00149.
   Web of Science ®Google Scholar
• Cai, Z. W. 2001. Estimating a distribution function for censored time series data. Journal of Multivariate Analysis 78 (2):299–318. doi:10.1006/jmva.2000.1953.
   Web of Science ®Google Scholar
• Doukhan, P. 1994. Mixing: Properties and examples. Lecture notes in statistics. Vol. 85. New York: Springer-Verlag.
   Google Scholar
• Eastoe, E. F., C. J. Halsall, J. E. Heffernan, and H. Hung. 2006. A statistical comparison of survival and replacement analyses for the use of censored data in a contaminant air database: A case study from the Canadian Arctic. Atmospheric Environment 40 (34):6528–40. doi:10.1016/j.atmosenv.2006.05.073.
   Web of Science ®Google Scholar
• Fan, J., and T. H. Yim. 2004. A crossvalidation method for estimating conditional densities. Biometrika 91 (4):819–34. doi:10.1093/biomet/91.4.819.
   Web of Science ®Google Scholar
• Ferraty, F., A. Laksaci, and P. Vieu. 2005. Functional time series prediction via conditional mode estimation. Comptes Rendus Mathematique 340 (5):389–92. doi:10.1016/j.crma.2005.01.016.
   Web of Science ®Google Scholar
• Gussoum, Z., and E. Ould-Saïd. 2008. On nonparametric estimation of the regression function under random censorship model. Statistics & Decisions 26 (3):159–77.
   Google Scholar
• Gussoum, Z., and E. Ould-Saïd. 2010. Kernel regression uniform rate estimation for censored data under α−mixing condition. Electronic Journal of Statistics 4:117–32.
   Web of Science ®Google Scholar
• Gussoum, Z., and E. Ould-Saïd. 2012. Central limit theorem for the kernel estimator of the regression function for censored time series. Journal of Nonparametric Statistics 24 (2):379–97.
   Web of Science ®Google Scholar
• Hall, P., and C. C. Heyde. 1980. Martingale limit theory and its application. New York: Springer-Verlag.
   Google Scholar
• Hyndman, R. J., D. M. Bashtannyk, and G. K. Grunwald. 1996. Estimating and visualizing conditional densities. Journal of Computational and Graphical Statistics 5 (4):315–36. doi:10.2307/1390887.
   Google Scholar
• Izbicki, R., and A. B. Lee. 2017. Converting high-dimensional regression to high-dimensional conditional density estimation. Electronic Journal of Statistics 11 (2):2800–31. doi:10.1214/17-EJS1302.
   Web of Science ®Google Scholar
• Khardani, S., and S. Semmar. 2014. Nonparametric conditional density estimation for censored data based on a recursive kernel. Electronic Journal of Statistics 8 (2):2541–56. doi:10.1214/14-EJS960.
   Web of Science ®Google Scholar
• Kim, C., M. Oh, S. J. Yang, and H. Choi. 2010. A local linear estimation of conditional hazard function in censored data. Journal of the Korean Statistical Society 39 (3):347–55. doi:10.1016/j.jkss.2010.03.002.
   Web of Science ®Google Scholar
• Liang, H. Y., and J. I. Baek. 2016. Asymptotic normality of conditional density estimation with left-truncated and dependent data. Statistical Papers 57 (1):1–20. doi:10.1007/s00362-014-0635-1.
   Web of Science ®Google Scholar
• Liang, H. Y., and J. de Uña-Álvarez. 2011. Asymptotic properties of conditional quantile estimator for censored dependent observations. Annals of the Institute of Statistical Mathematics 63 (2):267–89. doi:10.1007/s10463-009-0230-8.
   Web of Science ®Google Scholar
• Liang, H.-Y., and M. C. del Iglesias-Pérez. 2018. Weighted estimation of conditional mean function with truncated, censored and dependent data. Statistics 52 (6):1249–69. doi:10.1080/02331888.2018.1506923.
   Web of Science ®Google Scholar
• Liang, H. Y., and L. Peng. 2010. Asymptotic normality and Berry-Esseen results for conditional density estimator with censored and dependent data. Journal of Multivariate Analysis 101 (5):1043–54. doi:10.1016/j.jmva.2010.01.002.
   Web of Science ®Google Scholar
• Lipsitz, S. R., and J. G. Ibrahim. 2000. Estimation with correlated censored survival data with missing covariates. Biostatistics 1 (3):315–27. doi:10.1093/biostatistics/1.3.315.
   PubMedGoogle Scholar
• Rachdi, M., A. Laksaci, J. Demongeot, A. Abdali, and F. Madani. 2014. Theoretical and practical aspects of the quadratic error in the local linear estimation of the conditional density for functional data. Computational Statistics & Data Analysis 73:53–68. doi:10.1016/j.csda.2013.11.011.
   Web of Science ®Google Scholar
• Spierdijk, L. 2008. Nonparametric conditional hazard rate estimation: A local linear approach. Computational Statistics & Data Analysis 52 (5):2419–34. doi:10.1016/j.csda.2007.08.007.
   Web of Science ®Google Scholar
• Stute, W. 1993. Almost sure representation of the product-limit estimator for truncated data. The Annals of Statistics 21 (1):146–56. doi:10.1214/aos/1176349019.
   Web of Science ®Google Scholar
• Volkonskii, V. A., and Y. A. Rozanov. 1959. Some limit theorems for random functions. Theory of Probability & Its Applications 4 (2):178–97. doi:10.1137/1104015.
   Google Scholar