Above are papers related to the following paper: [Paper_ID] Paper #1119623. 	[Title] Interval Censored Survival Data: A Review of Recent Progress [Abstract] We review estimation in interval censoring models, including nonparametric estimation of a distribution function and estimation of regression models. In the non-parametric setting, we describe computational procedures and asymptotic properties of the nonparametric maximum likelihood estimators. In the regression setting, we focus on the proportional hazards, the proportional odds and the accelerated failure time semiparametric regression models. Particular emphasis is given to calculation of the Fisher information for the regression parameters. We also discuss computation of the regression parameter estimators via profile likelihood or maximization of the semi-parametric likelihood, distributional results for the maximum likelihood estimators, and estimation of (asymptotic) variances. Some further problems and open questions are also reviewed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #375825. 	[Title] Double Censoring: Characterization and Computation of the Nonparametric Maximum Likelihood Estimator [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #111770. 	[Title] Efficient Estimation for the Cox Model with Interval Censoring [Abstract] The maximum likelihood estimator (MLE) for the proportional hazards model with current status data is studied. It is shown that the MLE for the regression parameter is asymptotically normal with p n-convergence rate and achieves the information bound, even though the MLE for the baseline cumulative hazard function only converges at n 1=3 rate. Estimation of the asymptotic variance matrix for the MLE of the regression parameter is also considered. To prove our main results, we also establish a general theorem showing that the MLE of the finite dimensional parameter in a class of semiparametric models is asymptotically efficient even though the MLE of the infinite dimensional parameter converges at a rate slower than The results are illustrated by applying them to a data set from a tumoriginicity study. 1. Introduction In many survival analysis problems, we are interested in the p Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #421481. 	[Title] Bootstrapping Z Estimators [Abstract] We prove a general bootstrap theorem for possibly infinite-dimensional Zestimators which builds on the recent infinite-dimensional Ztheorem due to Van der Vaart (1995). Our result extends finite-dimensional results of this type for the bootstrap due to Arcones and Gine (1992), Lele (1991), and Newton and Raftery (1994). We sketch three examples of models with infinite-dimensional parameter spaces fi as applicatons of our general theorem. Please summarise all the given information.

Paper #1119623 has the title `Interval Censored Survival Data: A Review of Recent Progress  
` and the abstract `We review estimation in interval censoring models, including nonparametric estimation of a distribution function and estimation of regression models. In the non-parametric setting, we describe computational procedures and asymptotic properties of the nonparametric maximum likelihood estimators. In the regression setting, we focus on the proportional hazards, the proportional odds and the accelerated failure time semiparametric regression models. Particular emphasis is given to calculation of the Fisher information for the regression parameters. We also discuss computation of the regression parameter estimators via profile likelihood or maximization of the semi-parametric likelihood, distributional results for the maximum likelihood estimators, and estimation of (asymptotic) variances. Some further problems and open questions are also reviewed. 
`.
Question: Which category should Paper #1119623 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1119623 has the title `Interval Censored Survival Data: A Review of Recent Progress  
` and the abstract `We review estimation in interval censoring models, including nonparametric estimation of a distribution function and estimation of regression models. In the non-parametric setting, we describe computational procedures and asymptotic properties of the nonparametric maximum likelihood estimators. In the regression setting, we focus on the proportional hazards, the proportional odds and the accelerated failure time semiparametric regression models. Particular emphasis is given to calculation of the Fisher information for the regression parameters. We also discuss computation of the regression parameter estimators via profile likelihood or maximization of the semi-parametric likelihood, distributional results for the maximum likelihood estimators, and estimation of (asymptotic) variances. Some further problems and open questions are also reviewed. 
`.
Question: Which category should Paper #1119623 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods