Bayesian Prediction Bounds for the Exponential-Type Distribution Based on Ordered Ranked Set Sampling

2016 ◽  
Vol 31 (1) ◽  
Author(s):  
Mohammed S. Kotb
Keyword(s):  
Exponential Type ◽  
Bayesian Prediction ◽  
Prediction Intervals ◽  
Prior Density ◽  
Type Distribution ◽  
Ranked Set Sample ◽  
Cumulative Function ◽  
Sample Method ◽  
Special Case ◽  
Sample Case

AbstractWe suggest a ranked set sample method to improve Bayesian prediction intervals. The paper deals with the Bayesian prediction intervals in the context of an ordered ranked set sample from a certain class of exponential-type distributions. A proper general prior density function is used and the predictive cumulative function is obtained in the two-sample case. The special case of linear exponential distributed observations is considered and completed with numerical results.

Bayesian Prediction Bounds for the Exponential-type Distribution Based on Generalized Progressive Hybrid Censoring Scheme

2018 ◽  
Vol 33 (2) ◽  
pp. 93-101 ◽  
Author(s):  
Mohammed S. Kotb
Keyword(s):  
Censored Data ◽  
Reliability Function ◽  
Conjugate Prior ◽  
Underlying Distribution ◽  
Hybrid Censoring ◽  
Type Distribution ◽  
The One ◽  
Censoring Scheme ◽  
Special Case ◽  
Sample Case

Abstract This paper deals with predicting censored data in a general form for the underlying distribution based on generalized progressive hybrid censoring scheme. A conjugate prior is used and the predictive reliability function is obtained in the one-sample case. The special case of linear exponential distributed observations is considered and completed with numerical results.

A Characterization of the Exponential-Type Distribution

Biometrika ◽  
1963 ◽  
Vol 50 (1/2) ◽  
pp. 205 ◽  
Author(s):  
G. P. Patil
Keyword(s):  
Exponential Type ◽  
Type Distribution

Construction of Bayesian Prediction Intervals for Smart Systems

2013 ◽  
Author(s):  
Mami T. Wentworth ◽  
Ralph C. Smith
Keyword(s):  
Measurement Errors ◽  
Smart Material ◽  
Bayesian Prediction ◽  
Prediction Intervals ◽  
Smart Systems ◽  
Mcmc Algorithms ◽  
Quantity Of Interest ◽  
Input Parameters ◽  
Quantities Of Interest

In this paper, we employ adaptive Metropolis algorithms to construct densities for parameters and quantities of interest for models arising in the analysis of smart material structures. In the first step of the construction, MCMC algorithms are used to quantify the uncertainty in parameters due to measurement errors. We then combine uncertainties from the input parameters and measurement errors, and construct prediction intervals for the quantity of interest by propagating uncertainties through the models.

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