Journal of Statistical Research of Iran
Volume:11 Issue: 2, 2015

In this paper, we consider a flexible skew-generalized normal distribution. This distribution is denoted by $FSGN(/lambda _1, /lambda _2; /theta)$. It contains the normal, skew-normal (Azzalini, 1985), skew generalized normal (Arellano-Valle et al., 2004) and skew flexible-normal (Gomez et al., 2011) distributions as special cases. Some important properties of this distribution are established. Also, the practical usefulness of FSGN is illustrated via a well known real data set.: In this paper, we consider a flexible skew-generalized normal distribution. This distribution is denoted by $FSGN(/lambda _1, /lambda _2; /theta)$. It contains the normal, skew-normal (Azzalini, 1985), skew generalized normal (Arellano-Valle et al., 2004) and skew flexible-normal (Gomez et al., 2011) distributions as special cases. Some important properties of this distribution are established. Also, the practical usefulness of FSGN is illustrated via a well known real data set.

Doubly censoring scheme, which includes left as well as right censored observations, is frequently observed in practical studies. In this paper we introduce a new interval say tracking interval for comparing the two rival models when the data are doubly censored. We obtain the asymptotic properties of maximum likelihood estimator under doubly censored data and drive a statistic for testing the null hypothesis that the proposed non-nested models are equally close to the true model against the alternative hypothesis that one model is closer when we are faced with an experimental situation. Monte Carlo simulations are performed to observe the behavior of the theoretical results, and the proposed methodology is illustrated with data from spreading of the micro plasma droplets. We also perform the statistical analysis of these data using the probability models including Weibull, Burr type XII, Burr type III and inverse Weibull distributions. One important result of this study is that the Burr type XII distribution, in contrast to inverse Weibull distribution, may describe more closely to Weibull distribution for spread factor data under doubly censored sample.

In this paper, we discuss different estimators of the records Weibull distribution parameters and also we apply the Kullback-Leibler divergence of survival function method to estimate record Weibull parameters. Finally, these estimators have been compared using Monte Carlo simulation and suggested good estimators.

In this paper we derive some unbiased estimators of the population mean under simple inverse sampling with replacement, using the class of Hansen-Hurwitz and Horvitz-Thompson type estimators and the post-stratification approach. We also compare the efficiency of resulting estimators together with Murthy's estimator. We show that in despite of general belief, the strategy consisting of inverse sampling with Murthy's estimator is highly less efficient when the target population is rare, whereas it can be more efficient when subpopulation means are closed. In fact, for inverse sampling to be highly efficient design one should know the population structure and then use an appropriate estimator.

In this paper, a four parameters extension of the generalized Lindley distribution is introduced. The new distribution includes the power Lindley, Lindley, generalized (Stacy) gamma, gamma, Weibull, Rayleigh, exponential and half-normal distribution. Several statistical properties of the distribution are explored. Then, a bivariate version of the proposed distribution is derived. Using a simulation study, some estimation methods for the parameters of the distribution are compared. Finally, a real data application illustrates the performance of our proposed distribution.

Several characterizations of a New Modified Weibull distribution, introduced by Doostmoradi et al. (2014), are presented. These characterizations are based on: (i) truncated moment of a function of the random variable; (ii) the hazard function; (iii) a single function of the random variable; (iv) truncated moment of certain function of the 1st order statistic.