Inference for the generalized Rayleigh distribution based on progressively censored data

Mohammad Z. Raqab; Mohamed T. Madi

doi:10.1016/j.jspi.2011.04.016

Inference for the generalized Rayleigh distribution based on progressively censored data

Mohammad Z. Raqab
, Mohamed T. Madi

Research output: Contribution to journal › Article › peer-review

45 Citations (Scopus)

Abstract

In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.

Original language	English
Pages (from-to)	3313-3322
Number of pages	10
Journal	Journal of Statistical Planning and Inference
Volume	141
Issue number	10
DOIs	https://doi.org/10.1016/j.jspi.2011.04.016
Publication status	Published - Oct 2011

Keywords

Bayesian estimation
Bayesian prediction
Generalized rayleigh distribution
Gibbs and metropolis sampling
Importance sampling
Maximum likelihood estimation

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1016/j.jspi.2011.04.016

Cite this

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title = "Inference for the generalized Rayleigh distribution based on progressively censored data",

abstract = "In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.",

keywords = "Bayesian estimation, Bayesian prediction, Generalized rayleigh distribution, Gibbs and metropolis sampling, Importance sampling, Maximum likelihood estimation",

author = "Raqab, \{Mohammad Z.\} and Madi, \{Mohamed T.\}",

note = "Funding Information: The authors are grateful to the deanship of scientific research at King Saud University and the Faculty of Business and Economics at UAE University for supporting this research project.",

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AU - Raqab, Mohammad Z.

AU - Madi, Mohamed T.

N1 - Funding Information: The authors are grateful to the deanship of scientific research at King Saud University and the Faculty of Business and Economics at UAE University for supporting this research project.

PY - 2011/10

Y1 - 2011/10

N2 - In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.

AB - In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.

KW - Bayesian estimation

KW - Bayesian prediction

KW - Generalized rayleigh distribution

KW - Gibbs and metropolis sampling

KW - Importance sampling

KW - Maximum likelihood estimation

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DO - 10.1016/j.jspi.2011.04.016

M3 - Article

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SN - 0378-3758

VL - 141

SP - 3313

EP - 3322

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 10

ER -

Inference for the generalized Rayleigh distribution based on progressively censored data

Abstract

Keywords

ASJC Scopus subject areas

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