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