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 are described. The reliability and mean residual life of such systems with intact components at time t are investigated using a trivariate binomial model. Considering a Farlie-Gumbel-Morgenstern family, some graphical representations are given.</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
<p style="margin: 0px;">‎This paper presents a new finite mixture model using the normal mean-variance‎</p>
<p style="margin: 0px;">‎mixture of Birnbaum-Saunders distribution‎. ‎The proposed model is multimodal with wider‎</p>
<p style="margin: 0px;">‎range of skewness and kurtosis‎, ‎and is beneficial for modelling data with large asymmetric‎</p>
<p style="margin: 0px;">‎properties in the various theoretic and applied statistical problems‎. ‎The maximum likelihood‎</p>
<p style="margin: 0px;">‎estimates of the model's parameters are computed iteratively by simple EM-type algorithm‎.</p>
<p style="margin: 0px;">‎Proposing a simulation study to illustrate finite sample properties and performance of the obtained‎</p>
<p style="margin: 0px;">‎estimators‎, ‎we demonstrate the usefulness of the new model by analysing a real data example‎. </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 Kundu and Gupta [An extension of the generalized exponential distribution, Statistical Methodology 8 (2011), pp 485-496]. In this paper we consider the extended generalized exponential distribution with known shape parameters $alpha$ and $beta$. At first, the exact expressions for the single 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>
Gholamhossein HamedaniOn the Bayesian Sequential Change-Point Detection
http://jirss.irstat.ir/browse.php?a_id=381&sid=1&slc_lang=en
<pre 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. We discuss a Bayesian approach in the context of statistical process control: at an unknown time </span><span style=" color:#008000;">$tau$</span><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=" text-decoration: underline; color:#000000;">posteriori</span><span style=" color:#000000;"> 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=" text-decoration: underline; color:#000000;">Shiryaev's</span><span style=" color:#000000;"> 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></pre>
Gholamhossein Gholami