Alireza Safariyan; Reza Arabi Belaghi
Abstract
In this paper, the probability of failure-free operation until time t, along with the probability of stress-strength, based on progressive censoring data is studied in a family of lifetime distributions. Since the number of data in a progressive censoring scheme is usually reduced, so shrinkage methods ...
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In this paper, the probability of failure-free operation until time t, along with the probability of stress-strength, based on progressive censoring data is studied in a family of lifetime distributions. Since the number of data in a progressive censoring scheme is usually reduced, so shrinkage methods have been used to improve the classical estimator. For estimation purposes, the preliminary test and Stein-type shrinkage estimators are proposed and their exact distributional properties are derived. For numerical superiority demonstration of the proposed estimation strategies, some improved bootstrap confidence intervals, are constructed. The theoretical results are illustrated by a real data examples and an extensive simulation study. Simulation shreds of evidence revealed that our proposed shrinkage strategies perform well in the estimation of parameters based on progressive censoring data.
Statistical Simulation
Zahra Zandi; Hossein Bevrani; Reza Arabi Belaghi
Abstract
In this paper, we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage, {color{blue} preliminary test}, shrinkage {color{blue} ...
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In this paper, we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage, {color{blue} preliminary test}, shrinkage {color{blue} preliminary test}, shrinkage, and positive shrinkage estimators along with the unrestricted maximum likelihood and restricted estimators. The random effects are considered as nuisance parameters. We conduct a Monte Carlo simulation study to evaluate the performance of each estimator in the sense of simulated relative efficiency. The results of simulation study reveal that the proposed estimation strategies perform more better than {color{blue} the} maximum likelihood method. The proposed estimators are applied to a real dataset to appraise their performance.
