JOURNAL OF STATISTICAL SCIENCES
Year:2014 | Volume:7 | Issue:2|Papers Count:7

One of the issues in reviewing the performance of a financial market is existence of long-term memory. Since for a financial time series, we may find this feature in the volatility. So reviewing in volatility has been considered by many economists. A common method for identification and modeling of long-term memory in the volatility is to use FIGARCH models. In this paper, we identify and model long-term memory in the data exchange rates volatility (EUR/IRR). According to the statistical properties of skewness, heavy tail and excess kurtosis of data, assuming normal residuals being rejected and therefore cannot identify model by using common methods. The data structure looks NIG distribution is a good choice for the distribution of residuals. Hence with this assumption, we again identify model. The results show a good selection for data is FIGARCH-NIG model.

In this paper, some various families constructed from the logit of the generalized Beta, Beta، Kumar, generalized Gamma, Gamma, Weibull, log gamma and Logistic distributions are reviewed. Then a general family of distributions generated from the logit of the normal distribution is proposed. A special case of this family, Normal-Uniform distribution, is defined and studied. Various properties of the distribution are also explored. The maximum likelihood and minimum spacings estimators of the parameters of this distribution are obtained. Finally, the new distribution is effectively used to analysis a real survival data set.

In this paper, the pseudo-likelihood estimators and the limiting distribution of the score test statistic associated with several hypothesis tests such as unit root test for the linear regression models with stationary and nonstationary residuals are calculated. The limiting behavior of theses test statistics by using a simpler approach of the original presentation is derived. Also by using a Mont Carlo method, it is shown that the derived pseudo-likelihood estimators are appropriate. The quantiles of the limiting distribution of the test statistic for a unit root are also calculated and a new table is provided which can be used by researchers for the unit root test.

In this paper the likelihood and Bayesian inference of the stress-strength reliability are considered based on record values from proportional and proportional reversed hazard rate models. Then inference of the stress-strength reliability based on lower record values from some generalized distributions are also considered. Next the likelihood and Bayesian inference of the stress-strength model based on upper record values from Gompertz, Burr type XII, Lomax and Weibull distributions are considered. The ML estimators and their properties are studied. Likelihood-based confidence intervals, exact, as well as the Bayesian credible sets and bootstrap interval for the stress-strength reliability in all distributions are obtained. Simulation studies are conducted to investigate and compare the performance of the intervals.

In two level modeling, random effect and error's normality assumption is one of the basic assumptions. Violating this assumption leads to incorrect inference about coefficients of the model. In this paper, to resolve this problem, we use skew normal distribution instead of normal distribution for random and error components. Also, we show that ignoring positive (negative) skewness in the model causes overestimating (underestimating) in intercept estimation and underestimating (overestimating) in slope estimation by a simulation study. Finally, we use this model to study relationship between shift work and blood cholesterol.

Although the measurement error exists in the most scientific experiments, in order to simplify the modeling, its presence is usually ignored in statistical studying. In this paper, various approaches on estimating the parameters of multilevel models in presence of measurement error are studied. In addition, to improve the parameter estimates in this case, a new method is proposed which has high precision and reasonable convergence rate in compare with previous common approaches. Also, the performance of the proposed method as well as usual approaches are evaluated and compared using simulation study and analyzing real data of the income-expenditure of some households in Tehran city in 2008.

In this paper, first the Bhattacharray and Kshirsagar bounds are introduced and then the multiparameter Bhattacharyya bound is presented in simpler and understandable form. Furthermore, the multiparameter Kshirsagar lower bound, which has not been studied yet, is obtained. Finally, by presenting some example of Log-normal distribution, the bounds are computed and compared.