Journal of the Iranian Statistical Society
http://jirss@irstat.ir
Journal of The Iranian Statistical Society - Journal articles for year 2017, Volume 16, Number 1Yektaweb Collection - http://www.yektaweb.comen2017/6/11On the reliability of complex systems with three dependent components per element
http://jirss.irstat.ir/browse.php?a_id=344&sid=1&slc_lang=en
<p style="text-align: justify;">‎The complex system containing n elements‎, ‎each having three dependent components‎, ‎is described‎. ‎The reliability function of such systems is investigated using a trivariate binomial model‎. ‎In addition‎, ‎the mean residual life function of a complex system with intact components at time t is derived‎. ‎The results are simplified for a trivariate Farlie-Gumbel-Morgenstern family with standard exponential marginal distribution functions‎. ‎The effect of various parameters on the reliability and mean residual life functions are studied via some graphical representations.</p>
Mostafa RazmkhahAdmissibility in a One Parameter Non-regular Family with Squared-log Error Loss Function
http://jirss.irstat.ir/browse.php?a_id=347&sid=1&slc_lang=en
<p>‎Consider an estimation problem in a one-parameter non-regular distribution when both endpoints of the support depend on a single parameter‎. ‎In this paper‎, ‎we give sufficient conditions for a generalized Bayes estimator of a parametric function to be admissible‎. ‎Some examples are given‎. ‎</p>
Shirin Moradi ZahraieOn the Finite Mixture Modelling via Normal Mean-variance Birnbaum-Saunders Distribution
http://jirss.irstat.ir/browse.php?a_id=362&sid=1&slc_lang=en
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<p style="margin: 0px;">‎This paper presents a new finite mixture model using the normal mean-variance‎ ‎mixture of Birnbaum-Saunders distribution‎. ‎The proposed model is multimodal with wider‎ ‎ranges of skewness and kurtosis‎. ‎Moreover‎, ‎it is useful for modeling highly asymmetric data in various theoretical and applied statistical problems‎. ‎The maximum likelihood‎ ‎estimates of the parameters of the model are computed iteratively by feasible‎ ‎EM algorithm‎. ‎To illustrate the finite sample properties and performance of the‎ ‎estimators‎, ‎we conduct a simulation study and illustrate the usefulness of the new model by analyzing a real dataset‎.</p>
Mehrdad NaderiInferences for Extended Generalized Exponential Distribution based on Order Statistics
http://jirss.irstat.ir/browse.php?a_id=318&sid=1&slc_lang=en
<p>‎Recently‎, ‎a new distribution‎, ‎named as extended generalized exponential distribution‎, ‎has been introduced by <span class="fontstyle0">Kundu and Gupta (2011)</span>. ‎In this paper‎, ‎we consider the extended generalized exponential distribution with known shape parameters α and β. ‎At first‎, ‎the exact expressions for marginal and product moments of order statistics are derived‎. ‎Then‎, ‎these values are used to obtain the necessary coefficients for the best linear unbiased estimators and L-moments estimators of the location and scale parameters‎. ‎The mean squared errors of these estimators are also given and compared‎.</p>
Maliheh AbbasnejadCharacterizations of Certain Marshall-Olkin Generalized Distributions
http://jirss.irstat.ir/browse.php?a_id=351&sid=1&slc_lang=en
<p>Several characterizations of Marshall-Olkin generalized distributions, introduced by Gui (2013) and by Al-Saiari et al. (2014) are presented. These characterizations are based on: (i) a simple relationship between two truncated moments ; (ii) the hazard function.</p>
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Gholamhossein HamedaniOn the Bayesian Sequential Change-Point Detection
http://jirss.irstat.ir/browse.php?a_id=381&sid=1&slc_lang=en
<p style="margin-top: 0px; margin-bottom: 0px;"><span style=" color:#000000;">The problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance </span>and signal detection<span style=" color:#000000;">. We discuss a Bayesian approach in the context of statistical process control: at an unknown time </span>τ<span style=" color:#000000;">, the process behavior changes and the distribution of the data changes from </span><span style=" color:#008000;">p0</span><span style=" color:#000000;"> to </span><span style=" color:#008000;">p1</span><span style=" color:#000000;">. Two cases are considered: (i) </span><span style=" color:#008000;">p0</span><span style=" color:#000000;"> and </span><span style=" color:#008000;">p1</span><span style=" color:#000000;"> are fully known, (ii) </span><span style=" color:#008000;">p0</span><span style=" color:#000000;"> and </span><span style=" color:#008000;">p1</span><span style=" color:#000000;"> belong to the same family of distributions with some unknown parameters θ<sub>1</sub>≠θ<sub>2</sub></span><span style=" color:#000000;">. We present a maximum a </span><span style=" color:#000000;">posteriori estimate of the change-point which, for the case (i), can be computed in a sequential manner. In addition, we propose the use of the </span><span style=" color:#000000;">Shiryaev's loss function. Under this assumption, we define a Bayesian stopping rule. For the Poisson distribution and in the two cases (i) and (ii), we obtain results for the conjugate prior.</span></p>
Gholamhossein Gholami