Estimation with Univariate " Mixed Case " Interval Censored Data

  title={Estimation with Univariate " Mixed Case " Interval Censored Data},
  author={Shuguang Song},
In this paper, we study the Nonparametric Maximum Likelihood Estimator (NPMLE) of univariate “Mixed Case” interval-censored data in which the number of observation times, and the observation times themselves are random variables. We provide a characterization of the NPMLE, then use the ICM algorithm to compute the NPMLE. We also study the asymptotic properties of the NPMLE: consistency, global rates of convergence with and without a separation condition, and an asymptotic minimax lower bound. 

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Publications referenced by this paper.
Showing 1-10 of 21 references

Estimation with bivariate interval censored data

S. Song
Ph. D. thesis, • 2001
View 8 Excerpts
Highly Influenced

Lectures on inverse problems

P. Groeneboom
In Lectures on Probability Theory and Statistics 1648, Lectures Notes in Mathematics (Edited • 1996
View 4 Excerpts
Highly Influenced

Weak Convergence and Empirical processes with Application to Statistics

A. W. Van der Vaart, J. A. Wellner
View 20 Excerpts
Highly Influenced

Information Bounds and Nonparametric Maximum Likelihood Estimation, DMV Seminar Band 19, Birkhäuser, Basel

P. Groeneboom, J. A. Wellner
View 5 Excerpts
Highly Influenced

The empirical distribution function with arbitrary grouped, censored and truncated data

B. W. Turnbull
J. Roy. Statist. Soc. Ser. B • 1976
View 6 Excerpts
Highly Influenced

Consistency of the GMLE with mixed case interval censored data

A. Schick, Q. Yu
Scand. J. Statist • 2000
View 4 Excerpts
Highly Influenced

On convergence of convex minorant algorithm for distribution estimation with interval-censored data

J. Aragón, D. Eberly
J. Comput. Graph • 1992
View 8 Excerpts
Highly Influenced

Nonparametric maximum likelihood estimators for interval censoring and deconvolution

P. Groeneboom
Technical Report 378, • 1991
View 5 Excerpts
Highly Influenced

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