Maximum Likelihood and Bayesian Estimation of Rayleigh with Partly Interval-Censored Case-I Data

In this research, we consider the time interval for estimating non-character parameter functions for a single parameter Rayleigh apportionment. First, we get the maximum probability estimators (MLE.s) for non-personal parameters. MLEs cannot be obtained in clear formats. We also consider Bayesian re...

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Bibliographic Details
Published inNeuroQuantology Vol. 18; no. 5; pp. 26 - 28
Main Author Ghafil, Wisam Kamil
Format Journal Article
LanguageEnglish
Published Bornova Izmir NeuroQuantology 2020
Subjects
Bayesian analysis
Computer simulation
Mathematical analysis
Maximum likelihood estimation
Monte Carlo simulation
Parameter estimation
Programming languages
Sampling methods
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Summary:In this research, we consider the time interval for estimating non-character parameter functions for a single parameter Rayleigh apportionment. First, we get the maximum probability estimators (MLE.s) for non-personal parameters. MLEs cannot be obtained in clear formats. We also consider Bayesian reasoning for nonpersonal parameters Bayes estimates and associated reliable periods cannot be we get in closed shapes. We use an important sampling technique to round (calculate) Bayes estimates and their associated reliable time periods. For in order to compare we also used the accurate method to calculate Bayes. estimaties and related reliable periods. Monte Carlo simulation is performed using the R programming language to compare the proposed fashion performance, and one data set was analyzed for illustration purposes. We take into account the Bayes forecast trouble based on observable sampling.
ISSN:1303-5150
1303-5150
DOI:10.14704/nq.2020.18.5.NQ20163