7 papers analyzed
These studies suggest various methods for statistical inference in middle-censored data, including self-consistent and nonparametric maximum likelihood estimators, geometric distributions, inverse regression, maximum likelihood and Bayes estimates, Gibbs sampling, and rank score tests.
Middle-censored data refers to a type of data where observations falling within a certain interval are unobservable. This presents unique challenges for statistical inference, requiring specialized methods to estimate parameters and make inferences. This synthesis explores various approaches and methodologies for handling middle-censored data, focusing on nonparametric, parametric, and Bayesian estimation techniques.
Nonparametric Estimation Techniques:
Maximum Likelihood Estimation (MLE):
Bayesian Estimation:
Inverse Regression and Diagnostic Tools:
Quantile Regression for Longitudinal Data:
The research on statistical inference for middle-censored data highlights several effective methodologies, including nonparametric estimators, maximum likelihood estimation, and Bayesian approaches. These methods are robust and versatile, applicable across various distributions and data setups. Inverse regression and quantile regression models further extend the toolkit for handling middle-censored data, providing valuable diagnostic and hypothesis testing capabilities. Collectively, these approaches ensure reliable and consistent parameter estimation and inference in the presence of middle-censoring.
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