Journal of Statistical Sciences

A New Series of Complementary Exponential Power Distribution

Aref Khanjari Idenak

In this paper a new compounding distribution with increasing, decreasing, bathtub shaped and unimodal hazard rate function is proposed. The new four-parameters distribution is a generalization of the complementary exponential power distribution. The raw-moments, density function of the order statistics, survival function, hazard rate function, quantiles, mean residual lifetime and reliability function are presented. The estimation of the new distribution in a special case Poisson complementary exponential power distribution is studied by the method of maximum likelihood and EM algorithm. Expression for asymptotic distribution for the maximum likelihood estimation of the parameters of the PCEP distribution are obtained and for determining the precision of the variance and covariance of the estimations, a simulation is used, Then experimental results are illustrated based on the real data set.

A New Inverse Entezar Weibull Distribution

Ali Doostmoradi

In this paper a new distribution function based on Weibull distribution is introduced. Then the characteristics of this new distribution are considered and a real data set is used to compare this distribution with some of the generalized Weibull distributions.

An Improved Mean Estimator in Unbalanced Ranked Set Samples

Ehsan Zamanzade

In this paper, an improved mean estimator for unbalanced ranked set samples is proposed. The estimator is obtained by using the fact that distribution function of order statistics are stochastically ordered. Also, it is showed that this estimator is convergent and has better performance than its empirical counterpart in unbalanced ranked set samples.

Stractural Priors for Bayesian Analysis of Complete and Incomplete Contingency Tables

Kamran Ghoreishi

In the Bayesian analysis of contingency tables, analysts commonly use special prior distributions for the parameters of log-linear models or the cell probabilities. But, in practice, sometimes there is some interpretive information which is rather on (generalized) odds ratios. So, it seems one will need a powerful approach so that he can model his prior believe on (generalized) odds ratios. Here, we refer to these priors as structural priors. In this paper we first introduce the general pattern of the structural priors. Then, since these priors have vast application in clinical trials and especially in the analysis of 2 x 2 complete and incomplete contingency tables, we obtain the corresponding structural priors, separately, under three conditions.

Bayesian D-Optimal Design for Poisson Regression Model with Random Effect

Sahar Mehrmansour

The main researches of optimum experimental designs for mixed effects have been concentrated on locally optimal designs. These designs are obtained based on the initial guess of parameters. Therefore, locally designs may be the best design but for wrong assumed model. Recently, Bayesian approach has been considered by researches when information about model parameters is available. In the present work, optimal design for the mixed effects Poisson regression model based on some prior distributions are considered and for two special cases of this models the Bayesian D-optimal designs are obtained for some representative values of variance of random effect. The results are compared to Poisson regression model without random effects.

Statistical Clustering of Shape Data

Mousa Golalizadeh

Recently, employing multivariate statistical techniques for data, that are geometrically random, made more attention by the researchers from applied disciplines. Shape statistics, as a new branch of stochastic geometry, constitute batch of such data. However, due to non-Euclidean feature of such data, adopting usual tools from the multivariate statistics to proper statistical analysis of them is not somewhat clear. How to cluster the shape data is studied in this paper and then its performance is compared with the traditional view of multivariate statistics to this subject via applying these methods to analysis the distal femur.

Quantitative Three-Stage Optional Randomized Response Model

Sayed Mohammad Reza Alavi

The randomized response technique is a procedure for collecting the information on sensitive characteristics without exposing the identity of the respondent. Optional randomized response models are based on the basic premise that a question may be sensitive for one respondent but may not be sensitive for another. In this paper a three stage optional randomized response model is proposed and its properties are discussed using simulation with R package. The mean and sensitivity level of household's income of students of Shahid Chamran University are estimated using this model.