Journal of Statistical Research of Iran
http://jsri.srtc.ac.ir
Journal of Statistical Research of Iran JSRI - Journal articles for year 2013, Volume 9, Number 2Yektaweb Collection - http://www.yektaweb.comen2013/3/11A Cumulative Residual Entropy Characterization of the Rayleigh Distribution and Related Goodness-of-Fit Test
http://jsri.srtc.ac.ir/browse.php?a_id=203&sid=1&slc_lang=en
<p dir="rtl" style="text-align: justify;"><span style="line-height: 1.6em;">Rayleigh distribution is widely used for life-time modeling and is important in electro vacuum devices and communication engineering. Rao et al. (2004) suggested the Cumulative Residual Entropy (CRE), which is the extension of the Shannon entropy to the the cumulative distribution function. In this paper, a general class of maximum CRE distributions is introduced and then we characterize the Rayleigh distribution and use it to construct a goodness-of-fit test for ascertaining appropriateness of such model. For constructing the test statistics, we use Cumulative residual Kullback-Leibler information (CKL) that was introduced by Baratpour and Habibi (2012). Critical values for various sample sizes determined by means of Monte Carlo simulations are presented for the test statistics. A Monte Carlo power analysis is performed for various alternatives and sample sizes in order to compare the proposed test with several existing goodness-of-fit tests based on the empirical distribution. We find that the proposed test has good power properties. The use of the proposed test is shown in an illustrative example.</span></p>
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S. Baratpour Parametric Empirical Bayes Test and Its Application to Selection of Wavelet Threshold
http://jsri.srtc.ac.ir/browse.php?a_id=60&sid=1&slc_lang=en
<p style="margin:0cmmargin-bottom:.0001pttext-align:justifytext-justify:
kashidatext-kashida:0%">In this article, we propose a new method for selecting level dependent threshold in wavelet shrinkage using the empirical Bayes framework. We employ both Bayesian and frequentist testing hypothesis instead of point estimation method. The best test yields the best prior and hence the more appropriate wavelet thresholds. The standard model functions are used to illustrate the performance of the proposed method and make comparisons with other traditional methods.<!--stripped--><!--stripped--></p>
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Adel MohammadpourSome Results on a Generalized Archimedean Family of Copulas
http://jsri.srtc.ac.ir/browse.php?a_id=61&sid=1&slc_lang=en
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kashidatext-kashida:0%">Durante et al. (2007) introduced a class of bivariate copulas depending on two generators which generalizes some known families such as the Archimedean copulas. In this paper we provide some result on properties of this family when the generators are certain univariate survival functions.<!--stripped--><!--stripped--></p>
Ali Dolati Some Improvment in the Estimation of Population Mean in Cluster Sampling
http://jsri.srtc.ac.ir/browse.php?a_id=62&sid=1&slc_lang=en
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kashidatext-kashida:0%">Gupta and Shabbir (2008) have suggested an alternative form of ratio-type estimator for estimating the population mean. In this paper, we introduced new estimators by mixing two, stratified and cluster sampling method. Then we improved these estimators by using auxiliary variables and introducing new estimators. For sampling in infinite populations with a high geographic dispersion, the population will be divided into some smaller sub-population which leads to dispersion reduction to some extent. This will affect the variance of the estimator. Additionally dividing the population will result in saving cost, time and eases calculations.<!--stripped--><!--stripped--></p>
M. SakizadehModel Confidence Set Based on Kullback-Leibler Divergence Distance
http://jsri.srtc.ac.ir/browse.php?a_id=63&sid=1&slc_lang=en
<p style="margin:0cmmargin-bottom:.0001pttext-align:justifytext-justify:
kashidatext-kashida:0%">Consider the problem of estimating true density, h(.) based upon a random sample X<sub>1</sub>,…, X<sub>n</sub>. In general, h(.)is approximated using an appropriate in some sense, see below) model f<sub>Ɵ</sub>(<i>x</i>). This article using Vuong's (1989) test along with a collection of <i>k</i>(><span style="font-size:8.0pt"> </span>2)<span style="font-size:7.0pt"> </span>non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(.).Application of such confidence set has been confirmed through a simulation study.<!--stripped--><!--stripped--></p>
G. Barmalzan Estimation for the Type-II Extreme Value Distribution Based on Progressive Type-II Censoring
http://jsri.srtc.ac.ir/browse.php?a_id=64&sid=1&slc_lang=en
<p style="margin:0cmmargin-bottom:.0001pttext-align:justifytext-justify:
kashidatext-kashida:0%">In this paper, we discuss the statistical inference on the unknown parameters and reliability function of type-II extreme value (EVII) distribution when the observed data are progressively type-II censored. By applying EM algorithm, we obtain maximum likelihood estimates (MLEs). We also suggest approximate maximum likelihood estimators (AMLEs), which have explicit expressions. We provide Bayes estimates using both the symmetric and asymmetric loss functions via squared error loss, LINEX loss, and general entropy loss functions. Bayes estimates are obtained using the idea of Lindley and Markov chain Monte Carlo techniques. Finally, Monte Carlo simulations are presented to illustrate the methods discussed in this paper. Analysis is also carried out for a real data set.<!--stripped--><!--stripped--></p>
K. Ahmadi