الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Weighted exponential distribution has the probability density
function whose shape is related closely to the shape of the
probability density functions of Weibull, gamma and
generalized exponential distribution. Therefore, we can use
this model instead of any of these distributions. Weighted
exponential distribution has been applied to a wide range of
situations including applications in engineering, medicine and
reliability.
Accelerated life tests have widespread applications in
manufacturing industries. Generally, information from tests at
high levels of stress is extrapolated, through a physically
reasonable statistical model, to obtain estimates of life or long
term performance at normal levels of stress. The major
assumption in accelerated life test is that the acceleration
factor is known, or the life-stress relationships is known or can
be assumed. In some cases, neither acceleration factor nor
life-stress relationships are known and cannot even be
assumed, that is, i.e., the data obtained from accelerated life
test cannot be extrapolated to use conditions. Therefore, in
such cases partially accelerated life test, which is a special
case of accelerated life test, is a more suitable test to be
performed, for which items are subject to both normal and
accelerated conditions.
The main purpose of this thesis is to make statistical
inferences (estimation and prediction) for weighted exponential
distribution constant-stress model under hybrid censoring
scheme. The thesis contains three chapters presented as
follows:
In Chapter 1, some of essential definitions and concepts
which will be used through-out this thesis are introduced. Also,
Description of the model as well as previous studies is stated.
At the end of this chapter we present description of the
problem.
In Chapter 2, estimation of the parameters of weighted
exponential distribution is obtained based on constant-stress
type-II hybrid censoring scheme. Maximum likelihood
estimation is used for this purpose. Also, Bayes estimation is
used under squared error loss and LINEX loss functions by
using Markov chain Monte Carlo method. Approximate
confidence intervals based on asymptotic normality of the
maximum likelihood estimates of the parameters are
constructed. Numerical simulation study is used to compare
different estimates.
In Chapter 3, the problem of predicting future items drawn
from weighted exponential distribution with constant-stress
partially accelerated life test, based on hybrid censoring
scheme, is considered. We present one-sample and twosample
prediction schemes. We construct predictive intervals
for future observations of weighted exponential distribution
items. Finally, Markov chain Monte Carlo method is used to
find Bayesian predictive intervals. Simulation study has been
executed and the results have been stated.