Efficient nonparametric estimation of distribution for current status censoring
Author:
Affiliation:
1. Department of Mathematical Sciences, University of UTDallas, Richardson, USA
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability,Statistics, Probability and Uncertainty
Reference38 articles.
1. Abramovich, F. and Ritov, Y. (2013). Elements of Statistical Decision Theory. Chapman and Hall, Boca Raton.
2. Anderson-Bergman, C. and Yu, Y. (2016). Computing the log concave npmle for interval censored data. Statistics and Computing 26 813–826.
3. Braun, J., Duchesne, T. and Stafford, J. (2005). Local likelihood density estimation for interval censored data. The Canadian Journal of Statistics 33 39-60.
4. Becker, D., Braun, W. and White, B. (2017). Interval-censored unimodal kernel density estimation via data sharpening. Journal of Statistical Computation and Simulation 87 2023-2037.
5. Birge, L. (1999). Interval censoring: a nonasymptotic point of view. Mathematical Methods of Statistics 8 285-298.
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