Journal of the Iranian Statistical Society
http://jirss@irstat.ir
Journal of The Iranian Statistical Society - Journal articles for year 2017, Volume 16, Number 2Yektaweb Collection - https://yektaweb.comen2017/12/10Prediction for Lindley Distribution Based on Type-II Right Censored Samples
http://jirss.irstat.ir/browse.php?a_id=358&sid=1&slc_lang=en
<p>‎Lindley distribution has received considerable attention in the statistical literature due to its simplicity‎. ‎In this paper‎, ‎we consider the problem of predicting the failure times of experimental units that are censored in a right-censored sample‎‎ when the underlying lifetime is Lindley distributed‎. ‎The maximum likelihood predictor‎, ‎the Best unbiased predictor and the conditional median predictor are derived‎. ‎Prediction intervals based on these predictors are considered‎. ‎We further propose two resampling-based procedures for obtaining the prediction intervals‎. ‎A numerical example is used to illustrate the methodology developed in this paper‎. ‎Finally‎, ‎a Monte Carlo simulation study is employed to evaluate the performance of different prediction methods‎.</p>
Akbar AsgharzadehSome Results on Weighted Cumulative Entropy
http://jirss.irstat.ir/browse.php?a_id=380&sid=1&slc_lang=en
<p><span class="fontstyle0">Considering Rao </span><span class="fontstyle2">et al. </span><span class="fontstyle0">(2004) and Di Crescenzo and Longobardi (2009) studies, Misagh </span><span class="fontstyle2">et al. </span><span class="fontstyle0">(2011) proposed a weighted information which is based on the cumulative entropy called </span><span class="fontstyle2">Weighted Cumulative Entropy </span><span class="fontstyle0">(WCE). The above-mentioned model is a </span><span class="fontstyle2">Shiftdependent Uncertainty Measure</span><span class="fontstyle0">. In this paper, we examine some of the properties of WCE and obtain some bounds for that. In order to estimate this information measure, we put forward empirical WCE. Furthermore, in some theorems, we have some characterization results. We explore that, if the WCE of the first (last) order statistic are equal for two distributions, then this two distributions will be equal.</span><br style="line-height: normal; text-align: -webkit-auto; text-size-adjust: auto;" >
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Malihe MiraliTwo-step Smoothing Estimation of the Time-variant Parameter with Application to Temperature Data
http://jirss.irstat.ir/browse.php?a_id=348&sid=1&slc_lang=en
<p>‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time points and then compute the final estimator at any time by smoothing the raw estimators‎. ‎We will call these estimators two-step local polynomial smoothing estimator and two-step kernel smoothing estimator‎. ‎We derive these two two-step smoothing estimators by modeling raw estimates of the time-variant parameter from any regression model or probability model and then establish a mathematical relationship between these two estimators‎. ‎Our two-step estimation method is applied to temperature data from Dhaka‎, ‎the capital city of Bangladesh‎. ‎Extensive simulation studies under different cross-sectional and longitudinal frameworks have been conducted to check the finite sample MSE of our estimators‎. ‎Narrower bootstrap confidence bands and smaller MSEs from application and simulation results show the superiority of the local polynomial smoothing estimator over the kernel smoothing estimator‎. </p>
Mohammed ChowdhuryImproved Estimation in Rayleigh type-II Censored Data under a Bounded Loss Utilizing a Point Guess Value
http://jirss.irstat.ir/browse.php?a_id=388&sid=1&slc_lang=en
<p>‎The problem of shrinkage testimation (test-estimation) for the Rayleigh scale‎ ‎parameter θ based on censored samples under the reflected‎ ‎gamma loss function is considered‎. We obtain the minimum risk‎ ‎estimator among a subclass and compute its risk‎. ‎A shrinkage‎ ‎testimator based on acceptance or rejection of a null hypothesis‎ <span class="fontstyle2">H</span><span class="fontstyle3">0 </span><span class="fontstyle0">: </span><span class="fontstyle2">θ </span><span class="fontstyle0">= </span><span class="fontstyle2">θ</span><span class="fontstyle3">0</span> is constructed‎, ‎where <span class="fontstyle2">θ</span><span class="fontstyle3">0</span> is a point‎ ‎guess value of θ‎. ‎The risk of the proposed shrinkage‎ ‎testimator is computed numerically and compared with the minimum‎ ‎risk estimator‎. ‎A data set is analyzed for illustrative‎ ‎purposes‎. ‎</p>
<p style="margin: 0px;"></p>Mehran Naghizadeh QomiOn Concomitants of Order Statistics from Farlie-Gumbel-Morgenstern Bivariate Lomax Distribution and its Application in Estimation
http://jirss.irstat.ir/browse.php?a_id=303&sid=1&slc_lang=en
<p>‎In this paper‎, ‎we have dealt with the distribution theory of concomitants of order statistics arising from Farlie-Gumbel-Morgenstern bivariate Lomax distribution‎. ‎We have discussed the estimation of the parameters associated with the distribution of the variable Y of primary interest‎, ‎based on the ranked set sample defined by ordering the marginal observations on an auxiliary variable X‎, ‎when (X,Y) follows a Farlie-Gumbel-Morgenstern bivariate Lomax distribution‎. ‎When the association parameter and the shape parameter corresponding to Y are known‎, ‎we have proposed four estimators‎, ‎viz.‎, ‎an unbiased estimator based on the Stokes' ranked set sample‎, ‎the best linear unbiased estimator based on the Stokes' ranked set sample‎, ‎the best linear unbiased estimator based on the extreme ranked set sample and the best linear unbiased estimator based on the multistage extreme ranked set sample for the scale parameter of the variable of primary interest‎. ‎The relative efficiencies of these estimators have also been worked out‎.</p>
Anne PhilipSkew Laplace Finite Mixture Modelling
http://jirss.irstat.ir/browse.php?a_id=409&sid=1&slc_lang=en
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<p style="margin: 0px;">‎This paper presents a new mixture model via considering the univariate skew Laplace distribution‎. ‎The new model can handle both heavy tails and skewness and is multimodal‎. ‎Describing some properties of the proposed model‎, ‎we present a feasible EM algorithm for iteratively‎ ‎computing maximum likelihood estimates‎. ‎We also derive the observed information matrix for obtaining‎ ‎the asymptotic standard error of parameter estimates‎. ‎The finite sample properties of the obtained estimators‎ ‎as ‎well ‎as‎ the consistency of the associated standard error of parameter estimates are investigated by a‎ ‎simulation study‎. ‎We also demonstrate the flexibility and usefulness of the new model by analyzing real data‎ ‎example‎. </p>
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Mehrdad Naderi