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In parameter driven models, the main problem is likelihood approximation and also parameter estimation. One approach to this problem is to apply simpler likelihoods such as composite likelihood. In this paper, we first introduce the parameter driven models and composite likelihood and then define a new model selection criterion based on composite likelihood. Finally, we demonstrate composite likelihood's capabilities in inferences and accurate model selection in parameter driven models throughout a simulation study.

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A sequence of observations in which only successive minimum (maximum) values are observed are called record values. One sampling scheme for generating record values is: "Data are obtained via the inverse sampling scheme, where items are presented sequentially and sampling is terminated when n'th record is observed''. In this plan, the expectation of inter record times are infinite and in practice the number of records are few. Under the assumption that the process of observing record values can be replicated, one may consider the repetition of inverse sampling plan to achieve the specific number of records. In the latter scheme, we assume $m$ independent samples are obtained sequentially from the parent distribution and only recod data are observed. Two sampling (consecutive and repetition) plan are compared with regard to Fisher information contained in the extracted record data and general results are obtained. The proposed procedure is illustrated by considering several life time distributions such as Exponential, Burr XII and Weibull.

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In this paper we consider a single server queue with two phase arrival and two phase services. Arrival are Poison variables with different rates. For each input, the server provides private service with exponential distribution. The rates of services are different. The policy of service is FCFS, where the server changes the king of service according to the customer in the front of queue. After the completion of each service, the server either goes for a vacation with probability (1-theta), or may continue to server the next customer with probability theta, if any. Otherwise, it remains in the system until a customer arrives. Vacation times are assumed to have exponential distribution. We obtain steady-state probability generating function for queue size distribution for each input and expected busy period.

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In this paper, we obtain the generalized least square, restricted generalized least square and shrinkage estimators for the regression vector parameter assuming that the errors have multivariate t distribution. Also we calculate their quadratic risks and propose the dominance order of the underlying estimators.

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The estimate of entropy (sample entropy), has been introduced by Vasicek (1976), for the first time. In this paper, we provide an estimate of entropy of order statistics, that is the extention of the entropy estimate. Then we present an application of the entropy estimate of order statistics as a test statistic for symmetry of distribution versus skewness. The proposed test has been compared with some other existing tests. A Monte Carlo simulation study shows that the proposed test has more power than the Park's (1999) test.

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Unified hybrid censoring scheme is a mixture of generalized Type-I and Type-II hybrid censoring schemes. In this paper, we mainly consider the analysis of unified hybrid censored data when the lifetime distribution of the individual item is a two-parameter generalized exponential distribution. It is observed that the maximum likelihood estimators can not be obtained in a closed form. We obtain the maximum likelihood estimates of the parameters by using Newton-Raphson algorithm. The Fisher information matrix has been obtained and it can be used for constructing asymptotic confidence intervals. We also obtain the Bayes estimates of the unknown parameters under the assumption of independent gamma priors using the importance sampling procedure. Simulations are performed to compare the performances of the different schemes and one data set is analyzed for illustrative purposes.

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Because of importance and popularity of the Normal distribution, the samples based on this distribution has been considered and the outliers are identified using cut-off values which are dependent on the sample size. A decision problem has been structured to obtain the optimal cut-off value. The problem is solved by a simulation study with a minimax rule.

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In this paper, we characterize symmetric distributions based on Renyi entropy of order statistics in subsamples. A test of symmetry is proposed based on the estimated Renyi entropy. Critical values of the test are computed by Monte Carlo simulation. Also we compute the power of the test under different alternatives and show that it behaves better that the test of Habibi and Arghami (1386).

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In sampling, arisen data with probability proportional to its length is called Length-bised. Nonparametric density estimation in length-biased sampling is more difficult than other states. One of the famous estimators in this context is the one introduced by Jones (1991). In this paper, we calculate the bandwidth parameter of this estimator by Bayes'method. The strong consistency of this estimator have been proved with a random Bandwidth. We have compared the performance of Bayes'method with cross validation by using simulation studies.

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In this paper, first a brief introduction of ranked set sampling is presented. Then, construction of confidence intervals for a quantile of the parent distribution based on ordered ranked set sample is given. Because the corresponding confidence coefficient is an step function, one may not be able to find the exact prescribed value. With this in mind, we introduce a new method and show that one can obtained an optimal confidence interval by appealing the proposed approach. We also compare the proposed scheme with the other existence methods.

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Nowadays, the use of various types of censoring plan in studies of lifetime engineering systems and industrial experiment are worthwhile. In this paper, by using the idea in Cramer and Iliopoulos (2010), an adaptive progressive Type-I censoring is introduced. It is assumed that the next censoring number is random variable and depends on the previous censoring numbers, previous failure times and censoring times. General distributional results are obtained in explicit analytic forms. It is shown that maximum likelihood estimators coincide with those in deterministic progressive Type-I censoring. Finally, in order to illustrate and make a comparison, simulation study is done for one-parameter exponential distribution.

 

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In some practical problems, obtaining observations for the variable of interest is costly and time consuming. In such situations, considering appropriate sampling schemes, in order to reduce the cost and increase the efficiency are worthwhile. In these cases, ranked set sampling is a suitable alternative for simple random sampling. In this paper, the problem of Bayes estimation of the parameter of Pareto distribution under squared error and LINEX loss functions is studied. Using a Monte Carlo simulation, for both sampling methods, namely, simple random sampling and ranked set sampling, the Bayes risk estimators are computed and compared. Finally, the efficiency of the obtained estimators is illustrated throughout using a real data set. The results demonstrate the superiority of the ranked set sampling scheme, therefore, we recommend using ranked set sampling method whenever possible.

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