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Identity for the NPMLE in Censored Data Models
Lifetime Data Analysis, 1998We derive an identity for nonparametric maximum likelihood estimators (NPMLE) and regularized MLEs in censored data models which expresses the standardized maximum likelihood estimators in terms of the standardized empirical process. This identity provides an effective starting point in proving both consistency and efficiency of NPMLE and regularized ...
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A random‐censoring Poisson model for underreported data
Statistics in Medicine, 2017A major challenge when monitoring risks in socially deprived areas of under developed countries is that economic, epidemiological, and social data are typically underreported. Thus, statistical models that do not take the data quality into account will produce biased estimates.
Guilherme Lopes de Oliveira +2 more
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Jeffreys priors for survival models with censored data
Journal of Statistical Planning and Inference, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
DE SANTIS, Fulvio +2 more
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A Quantile Survival Model for Censored Data
Australian & New Zealand Journal of Statistics, 2013SummaryIn this paper we propose a quantile survival model to analyze censored data. This approach provides a very effective way to construct a proper model for the survival time conditional on some covariates. Once a quantile survival model for the censored data is established, the survival density, survival or hazard functions of the survival time can
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1987
The censored normal regression model considered by Tobin (1958), also commonly known as the ‘tobit’ model, is the following: $$y_i^* = \beta {x_i} + {u_i}.\quad {u_i} \sim IN(0,{\sigma ^2})$$ The observed y i are related to y i * according to the relationship $$\eqalign{ & {y_i} = y_i^*\quad if\,y_i^* > {y_0} \cr & = {y_0}\quad \;\quad ...
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The censored normal regression model considered by Tobin (1958), also commonly known as the ‘tobit’ model, is the following: $$y_i^* = \beta {x_i} + {u_i}.\quad {u_i} \sim IN(0,{\sigma ^2})$$ The observed y i are related to y i * according to the relationship $$\eqalign{ & {y_i} = y_i^*\quad if\,y_i^* > {y_0} \cr & = {y_0}\quad \;\quad ...
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Semi-Markov models for partially censored data
Biometrika, 1978SUMMARY Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining ...
Lagakos, S. W., Sommer, C. J., Zelen, M.
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Cure rate model with interval censored data
Statistics in Medicine, 2007AbstractIn cancer trials, a significant fraction of patients can be cured, that is, the disease is completely eliminated, so that it never recurs. In general, treatments are developed to both increase the patients' chances of being cured and prolong the survival time among non‐cured patients.
Yang-Jin, Kim, Myoungshic, Jhun
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Semiparametric analysis of transformation models with censored data
Biometrika, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, KN, Jin, ZZ, Ying, ZL
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Progressive Censoring: Data and Models
2014The notion of progressive censoring is explained by introducing progressive Type-I and Type-II censoring in detail. The presentation includes detailed descriptions of the procedures as well as graphical illustrations and data. Additionally, progressive hybrid censoring is discussed.
N. Balakrishnan, Erhard Cramer
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Composite scale modeling in the presence of censored data
Reliability Engineering & System Safety, 2006A composite scale modeling approach can be used to combine several scales or variables into a single scale or variable. A typical application is to combine age and usage together to form a composite timescale model. The combined scale is expected to have better failure prediction capability than individual scales.
R. Jiang, Andrew K. S. Jardine
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