Two estimators of the mean of a counting process with panel count data

By Jon A. Wellner, Ying Zhang. 2000

206 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9aa71d97787725881ac59b22fbc8e69af341f2ed

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Citations in the corpus

Confidence Intervals for Current Status Data (2005, background): “We now formally introduce the current status model.”
AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS (2011, methodology): “For example, Wellner and Zhang (2000, 2007) propose non-parametric and semi-parametric estimators for panel count data using an independence likelihood, and Andersen (2004) uses pairwise likelihood in the proportional hazards model.”
Nonparametric k-sample tests with panel count data (2006, methodology): “This theorem can be viewed as a generalisation of the result given by Huang and Wellner (1995), since Wellner and Zhang (2000) found out that the characteristics of the nonparametric maximum pseudo-likelihood estimator of the mean function with panel count data are structurally similar to those of…”
Estimation of the mean function with panel count data using monotone polynomial splines (2007, methodology): “We express the pseudolikelihood and likelihood functions given in Wellner & Zhang (2000) using the I -spline functions and estimate the spline coefficients using the generalized Rosen algorithm proposed by Zhang & Jamshidian (2004).”
Semiparametric Estimation Methods for Panel Count Data Using Monotone B-Splines (2009, methodology): “One of the primary goals of this study was to determine the effect of treatment on suppressing the tumor recurrence; see, for example, Byar (1980), Wei, Lin, and Weissfeld (1989), Wellner and Zhang (2000, 2007), Sun and Wei (2000), and Lu, Zhang, and Huang (2007).”
A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data (2010, methodology): “…algorithm with W =−H to compute the monotone mean function estimates in the context of spline-based sieve non-parametric maximum likelihood analysis of panel count data and demonstrated the numerical advantage of this algorithm over the ICM algorithm used by Wellner & Zhang (2000).”
Likelihood Ratio Tests for Monotone Functions (2001, background): “…N i are observed only at irregular (random) times T ij K i j=1 , with perhaps a random number of observation times K i for the ith individual, Wellner and Zhang (2000) have referred to this type of data as " panel count data, " and have studied the nonparametric maximum likelihood estimator…”
Wiener processes with random effects for degradation data (2010, background): “2 are given in Appendix which are established similarly as in [23,24]).”
An Inverse Gaussian Process Model for Degradation Data (2010, unknown): “This metric d is equivalent to another common L2 metric d0 (Wellner and Zhang 2000): If P(K ≤ k0) = 1 for some k0 ∞, 1 2 d(α1, α2) ≤ d0(α1, α2) ≤ k0d(θ1, θ2), where d20(α1, α2) = (η1 − η2)2 + ∫ ( 1(t) − 2(t))2 dμ0(t) and μ0(B) = ∑∞k=1 P(N = k)∑kj=1 P(TN,j ∈ B|N = k).”