1
1735-1294
Statistical Research and Training Center - Statistical Centre of Iran
151
General
A New Goodness-of-Fit Test for a Distribution by the Empirical Characteristic Function
Towhidi
M.
Salmanpour
M.
1
9
2005
2
1
1
13
09
02
2016
09
02
2016
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]
147
General
Dispersive Ordering and k-out-of-n Systems
Khaledi
Baha-Eldin
1
9
2005
2
1
15
38
09
02
2016
09
02
2016
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]
150
General
Distribution Free Confidence Intervals for Quantiles Based on Extreme Order Statistics in a Multi-Sampling Plan
Razmkhah
M.
Ahmadi
J.
Khatib
B.
1
9
2005
2
1
39
52
09
02
2016
09
02
2016
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]
146
General
Determination of Optimal Sampling Design for Spatial Data Analysis
Khaledi Jafari
M.
Rivaz
F.
1
9
2005
2
1
53
60
09
02
2016
09
02
2016
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]
148
General
Empirical Bayes Estimation in Nonstationary Markov chains
Meshkani
R.
Billard
L.
1
9
2005
2
1
77
88
09
02
2016
09
02
2016
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.
149
General
Properties of Spatial Cox Process Models
Moller
J.
1
9
2005
2
1
89
106
09
02
2016
09
02
2016
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.