Journal of Statistical Sciences

Detecting Outliers in Normal Data Using Modified Z-Scores

MohammadVali Ahmadi

Because of importance and popularity of the Normal distribution, the samples based on this distribution has been considered and the outliers are identified using cut-off values which are dependent on the sample size. A decision problem has been structured to obtain the optimal cut-off value. The problem is solved by a simulation study with a minimax rule.

Haseman-Elston Regression Methods in Genetic Linkage Analysis

Hamid Alavimajd

One of the important problems that bring up in genetic fields is determining of loci of special gene in order to gene mapping and generating more effective drugs in medicine. Genetic linkage analysis is one important stage in this way. Haseman-Elston method is a quantitative statistical method that is used by biostatisticians and geneticists for genetic linkage analysis. The original Haseman-Elston method is presented in the year 1972 and ever after many investigators recommended some suggestions to make better it. In this article, we introduce the Haseman-Elston regression method and its extensions through 1972 to 2009. and finally we show performance of these methods in a practical example.

Separate Block Bootstrap Method for Determining the Precision Measures of the Variogram Parameters Estimator and Spatial Prediction

Nasrollah Iranpanah

Abstract: In many environmental studies, the collected data are usually spatially dependent. Determination of the spatial correlation structure of the data and prediction are two important problem in statistical analysis of spatial data. To do so, often, a parametric variogram model is fitted to the empirical variogram of the data by estimating the unknown parameters of the mentioned variogram. Since there are no closed formulas for the variogram parameters estimator, they are usually computed numerically. Therefore, the precision measures of the variogram parameters estimator and spatial prediction can be calculated using bootstrap methods. Lahiri (2003) proposed the moving block bootstrap method for spatial data, in which observations are divided into several moving blocks and resampling is done from them. Since, in this method, the presence of boundary observations in the resampling blocks have less selection chance than the other observations, therefore, the estimator of the precision measures would be biased. In this paper, revising the moving block bootstrap method, the separate block bootstrap method was presented for estimating the precision measures of the variogram parameters estimator and spatial prediction. Then its usage was illustrated in an applied example.

Comparsion of Markov Switching Autoregresive and Self Exciting Threshold Autoregresive Models for Fluctuations of Exchange Rate of Iran

Hamidreza Mostafaei

In 2002 the enforcement on policy unification of exchange rate caused dramatic decrease in the nominal price of Iran's Rial against U.S.dollar per on unit.For this reason due to the existence of unexpected and large change we cannot use the linear time series models for surveying the fluctuations of the rate of Iran's Rial change against U.S. dollar per on unit. In this paper we compare Self-Exciting threshold autoregressive and Markov switching autoregressive model. then it will be show that only the Markov switching autoregressive model being able to show the behaviors of Iran's exchange rate.

The Effect of Correlation on the Change of Entropy of the Maximum Entropy joint Distribution

Shahram Mansoury

Stochastically ordered random variables with given marginal distributions are combined into a joint distribution preserving the ordering and the marginals using a maximum entropy principle. A closed-form of the maximum entropy density function is obtained. Next we have compared the entropies of maximum entropy distributions, under two constraints The constraints are either prescription of marginal distributions and the marginals and covariance matrix.

Estimstion of Density Function in the Presence of Outliers

Mina Towhidi

One of the most important issues in inferential statistics is the existence of outlier observations. Since these observations have a great influence on fitted model and its related inferences, it is necessary to find a method for specifying the effect of outlier observations. The aim of this article is to investigate the effect of outlier observations on kernel density function estimation. In this article we have tried to represent a method for identification of outlier observations and their effect on kernel density function estimation by using forward search method

Another Family of Bivariate Distributions with Equivalent Independence and Uncorrelation

Reza Hashemi

Considering the characteristics of the bivariate normal distribution, in which uncorrelation of two random variables is equivalent to their independence, it is interesting to verify this issue in other distributions in other words whether or not the multivariate normal distribution is the only distribution in which uncorrelation is equivalent to independence. This paper aims to answer this question by presenting some concepts and introduce another family in which uncorrelation is equivalent to independence.