Statistical Research and Training Center - Statistical Centre of Iran Journal of Statistical Research of Iran JSRI 1735-1294 2 1 2005 9 1 A New Goodness-of-Fit Test for a Distribution by the Empirical Characteristic Function 1 13 EN M. Towhidi mtowhidi@susc.ac.ir M. Salmanpour mhi_salman@hotmail.com 10.18869/acadpub.jsri.2.1.1 Extended Abstract. Suppose n i.i.d. observations, X1, …, Xn, are available from the unknown distribution F(.), goodness-of-fit tests refer to tests such as H0 : F(x) = F0(x) against H1 : F(x) \$neq\$ F0(x). Some nonparametric tests such as the Kolmogorov--Smirnov test, the Cramer-Von Mises test, the Anderson-Darling test and the Watson test have been suggested by comparing empirical distribution, Fn(x), and the known distribution F0(x). The characteristic function is important in characterizing the probability distribution theoretically. Thus it have been expected that the empirical characteristic function, cn(t), can be used for suggesting a goodness-of-fit test...[To Continue click here] . characteristic function, consistent test, eigen values, goodness-of-fit test, multivariate central limit theorem, principal components method http://jsri.srtc.ac.ir/article-1-151-en.html http://jsri.srtc.ac.ir/article-1-151-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran Journal of Statistical Research of Iran JSRI 1735-1294 2 1 2005 9 1 Dispersive Ordering and k-out-of-n Systems 15 38 EN Baha-Eldin Khaledi bkhaledi@hotmail.com 10.18869/acadpub.jsri.2.1.15 Extended Abstract. The simplest and the most common way of comparing two random variables is through their means and variances. It may happen that in some cases the median of X is larger than that of Y, while the mean of X is smaller than the mean of Y. However, this confusion will not arise if the random variables are stochastically ordered. Similarly, the same may happen if one would like to compare the variability of X with that of Y based only on numerical measures like standard deviation etc. Besides, these characteristics of distributions might not exist in some cases. In most cases one can express various forms of knowledge about the underlying distributions in terms of their survival functions, hazard rate functions, mean residual functions, quantile functions and other suitable functions of probability distributions. These methods are much more informative than those based only on few numerical characteristics of distributions. Comparisons of random variables based on such functions usually establish partial orders among them. We call them as stochastic orders. Stochastic models are usually sufficiently ...[To continue click here] usual stochastic order, hazard rate order, likelihood ratio order, majorization, p-larger, schur functions, proportional hazard models, k-out-of-n systems, spacings http://jsri.srtc.ac.ir/article-1-147-en.html http://jsri.srtc.ac.ir/article-1-147-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran Journal of Statistical Research of Iran JSRI 1735-1294 2 1 2005 9 1 Distribution Free Confidence Intervals for Quantiles Based on Extreme Order Statistics in a Multi-Sampling Plan 39 52 EN M. Razmkhah razmkhah_m@yahoo.com J. Ahmadi ahmadi@math.um.ac.ir B. Khatib khatib_b@yahoo.com 10.18869/acadpub.jsri.2.1.39 Extended Abstract. Let Xi1 ,..., Xini   ,i=1,2,3,....,k  be independent random samples from distribution \$F^{alpha_i}\$،  i=1,...,k, where F is an absolutely continuous distribution function and \$alpha_i>0\$ Also, suppose that these samples are independent. Let Mi,ni and  Mchr('39')i,ni  respectively, denote the maximum and minimum of the ith sample. Constructing the distribution-free confidence intervals for quantiles of F based on these informations is the aim of this paper. Various cases have been studied and in each case, the exact non-parametric confidence intervals are obtained. First, we concentrate our attention to the maxima of the samples. Coverage probability of a confidence interval based on two different... [To Continue click here] Extreme order statistics, confidence interval, quantile, coverage probability, model, reversed hazard rate function. http://jsri.srtc.ac.ir/article-1-150-en.html http://jsri.srtc.ac.ir/article-1-150-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran Journal of Statistical Research of Iran JSRI 1735-1294 2 1 2005 9 1 Determination of Optimal Sampling Design for Spatial Data Analysis 53 60 EN M. Khaledi Jafari jafari-m@modares.ac.ir F. Rivaz rivaz@modares.ac.ir 10.18869/acadpub.jsri.2.1.53 Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here] Spatial data, spatial sampling design, optimality http://jsri.srtc.ac.ir/article-1-146-en.html http://jsri.srtc.ac.ir/article-1-146-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran Journal of Statistical Research of Iran JSRI 1735-1294 2 1 2005 9 1 Empirical Bayes Estimation in Nonstationary Markov chains 77 88 EN R. Meshkani mrmeshkani@gmail.com L. Billard lynne@stat.uga.edu 10.18869/acadpub.jsri.2.1.77 Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically distributed chains recorded collectively. Bayes estimates, empirical Bayes estimates, natural conjugate priors. http://jsri.srtc.ac.ir/article-1-148-en.html http://jsri.srtc.ac.ir/article-1-148-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran Journal of Statistical Research of Iran JSRI 1735-1294 2 1 2005 9 1 Properties of Spatial Cox Process Models 89 106 EN J. Moller jm@math.aau.dk 10.18869/acadpub.jsri.2.1.89 Probabilistic properties of Cox processes of relevance for statistical modeling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment properties and point process operations such as thinning, displacements, and superpositioning. We also discuss how to simulate specific Cox processes. Doubly stochastic process, edge effects, intensity, log Gaussian Cox process, mixed Poisson process, pair correlation function, permanent process, random displacements, shot noise Cox process, simulation, spatial point process, superposition, thinning. http://jsri.srtc.ac.ir/article-1-149-en.html http://jsri.srtc.ac.ir/article-1-149-en.pdf