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In this paper, based on the progressive type II right censored data, we consider estimates of MLE and AMLE of scale and shape parameters of weibull distribution. Also a new type of parameter estimation, named inverse estimation, is introdued for both shape and scale parameters of weibull distribution which is used from order statistics properties in it. We use simulations and study the biases and MSE 's of these three estimation procedures and then compare them with each other. At the end, two numerical examples are used to illustrate the proposed procedures.

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In many experiments about lifetime examination, we will faced on restrictions of time and sample size, which this factors cause that the researcher can’t access to all of data. Therefore, it is valuable to study prediction of unobserved values based on information of available data. in this paper we have studied the prediction of unobserved values in two status of one-sample and two-sample, when the parent distribution is the exponential distribution and imposed restriction is double censoring. in each case the interval prediction by given cover will be obtain. Finally, a numerical example is given to illustrate the procedures.

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In this article introduce the sequential order statistics. Therefore based on multiply Type-II censored sample of sequential order statistics, Bayesian estimators are derived for the parameters of one- and two- parameter exponential distributions under the assumption that the prior distribution is given by an inverse gamma distribution and the Bayes estimator with respect to squared error loss is calculated. Moreover, prediction of future failure time is considered. Finally in example Bayesian estimator and non-bayesian estimatores, namely the Best Linear Unbiased Estimator (BLUE) and Approximate Maximum Likelihood Estimator (AMLE) are derived.

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‎The censored data are widely used in statistical tests and parameters estimation‎. ‎In some cases e.g‎. ‎medical accidents which data are not recorded at the time of occurrence‎, ‎some methods such as interval censoring are used‎. ‎In this paper‎, ‎for a random sample uniformly distributed on the interval (0,θ) ‎the interval censoring have been used‎. ‎A consistent estimator of θ  and some asymptotically confidence intervals for θ are presented‎.
 

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The cumulative distribution and density functions of a product of some random variables following the power distribution with different parameters have been provided.
The corresponding characteristic and moment-generating functions are also derived.
We extend the results to the exponential variables and furthermore, some useful identities have been investigated in detail.

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In this article, the number of failures of a coherent system has been studied under the assumption that the lifetime of system components are non-distributed discrete and dependent random variables. First, the probability that exactly

i

Failure
i=0, ..., n-k,

in a system

$k$

From

n

Under the condition that the system at the time of monitoring

t

it works

it will be counted. In the following, this result has been generalized to other coherent systems. In addition, it has been shown that in the case of independence and co-distribution of component lifetimes, the probability obtained is consistent with the corresponding probability in the continuous state obtained in the existing literature. Finally, by presenting practical examples, the behavior of this probability has been investigated in the case that the system components have interchangeable and necessarily non-distributed lifetimes

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Actuarial studies treat insurance losses as random variables, and appropriate probabilistic models are sought to model them. Since losses are evaluated in terms of a unitary amount, distributions with positive support are typically used to model them. However, in practice, losses are often bounded due to policyholder conditions, which must be considered when modeling. While this is not a problem for univariate cases, it becomes complicated for multivariate cases. Copulas can be helpful in such situations, but studying the correlation is crucial in the first step. Therefore, this paper addresses the problem of investigating the effect of restricted losses on correlation in multivariate cases.
The Pearson correlation coefficient is a widely used measure of linear correlation between variables. In this study, we examine the correlation between two random variables and investigate the estimator of the correlation coefficient. Furthermore, we analyze a real-world dataset from an Iranian insurance company, including losses due to physical damage and bodily injury as covered by third-party liability insurance.
Upper and lower limits for both the Pearson correlation coefficient and its estimator were derived. The Copula method was employed to obtain the bounds for the correlation parameters, while order statistics were used to obtain the bounds for the sample correlation coefficient. Furthermore, two methods were used to determine the correlation between physical damage and bodily injury, and the results were compared.
Our findings suggest that the commonly used upper and lower bounds of -1 and +1 for the Pearson correlation coefficient may not always apply to insurance losses. Instead, our analysis reveals that narrower bounds can be established for this measure in such cases. The results of this study provide important insights into modeling insurance losses in multivariate cases and have practical implications for risk management and pricing decisions in the insurance industry.