الفهرس 
Title : Estimation of Parameters for Some Lifetime Distributions in the Presence of Outliers: Bayesian Approach
AbstractOnly 14 pages are availabe for public view
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Abstract
In statistics, the problem of outliers is an important in almost all
experimental fields. The existence of outliers in the data might distort the
estimated model parameters as well as the model’s goodness of fit. Outliers
deserve special consideration since even little deviations from the assumed model
can have a severe influence on the efficiency of parameter estimates.
In this thesis, the estimation of the entropy for the power function
distribution in the presence of outliers and homogenous case is obtained using the
methods of maximum likelihood and Bayesian estimations. The Markov Chain
Monte Carlo method using Gibbs sampling is developed due to the lack of
explicit forms for the Bayesian estimates.
A simulation study is implemented to compute and compare the performance of estimates in both methods with respect to absolute biases and mean squared errors. Application to real data set is given to confirm the results of study.
The estimation of entropy and extropy for Pareto distribution in the
presence of outliers and homogenous case is discussed in this thesis. The
maximum likelihood and Bayesian estimations using different loss functions are
applied to estimate the entropy measure. Markov Chain Monte Carlo procedure
using Metropolis-Hastings algorithm is used to generate posterior random
variables. Monte Carlo procedure using simulations is designed to implement the
precision of estimates for different sample sizes and number of outliers. The
performance of estimates is planned by experiments with real data.
Furthermore, estimating of stress-strength reliability model in the presence of outliers and homogenous case using the maximum likelihood and Bayesian methods is regarded. Bayesian estimators of = P(Y < X ) are derived by
considering the independent gamma priors. A simulation study is implemented to compute and compare the performance of estimates. Also, application to real-data is also used.