مجله علوم آماری
سال یازدهم شماره 2 (پیاپی 22، پاییز و زمستان 1396)

Stochastic processes are very important in statistics and probability, where finding upper and lower bounds of mean-square stochastic integral has led to a basic problem. In this paper we show that for mean-square differentiable stochastic process, the convexity condition in previous well-known results can be replaced by weaker conditions.

Semiparametric linear mixed measurement error models are extensions of linear mixed measurement error models to include a nonparametric function of some covariate. They have been found to be useful in both cross-sectional and longitudinal studies. In this paper first we propose a penalized corrected likelihood approach to estimate the parametric component in semiparametric linear mixed measurement error model and then using the case deletion and subject deletion analysis we survey the influence diagnostics in such models. Finally, the performance of our influence diagnostics methods are illustrated through a simulated example and a real data set.

In this paper the mixed Poisson regression model is discussed and a Poisson Birnbaum-Saunders regression model is introduced consider the over-dispersion. The Birnbaum-Saunders distribution is the mixture of two the generalized inverse Gaussian distributions, therefore it can be considered as an extension of traditional models.
Our proposed model has less dimensional parameter space than the Poisson- generalized inverse Gaussian regression model. We also show that the proposed model has a closed form for likelihood function and we obtain its moments. The EM algorithm is used to estimate the parameters and its efficiency is compared with conventional models by a simulation study. An analysis of a real data is provided for more illustration.

There are several methods for clustering time course gene expression data. But, these methods have limitations such as the lack of consideration of correlation over time and suffering of high computational.
In this paper, by introducing the non-parametric and semi parametric mixed effects model, this correlation over time is considered and by using penalized splines, computation burden dramatically reduced. At the end, using a simulation study the performance of the presented method is compared with previous methods and by using BIC criteria, the most appropriate model is selected. Also the proposed approach is illustrated in a real time course gene expression data set.

The standard Beta distribution is a suitable distribution for modeling the data that include proportions. In many situations which the data of proportions include a considerable number of zeros and ones, the inflated beta distributions are more appropriate. When probabilities of recording such observations are proportional to a nonnegative weight function, the recorded observations distributed as a weighted inflated Beta. This article focuses on the size biased inflated Beta distribution as a special case of weighted inflated Beta distribution with the power weight function. Some properties of this distribution is studied and its parameters are estimated using maximum likelihood and method of moments approaches. The estimators are compared via a simulation study. Finally, the real mortality data set is fitted for this model.

Regression analysis is done, traditionally, considering homogeneity and normality assumption for the response variable distribution. Whereas in many applications, observations indicate to a heterogeneous structure containing some sub-populations with skew-symmetric structure either due to heterogeneity, multimodality or skewness of the population or a combination of them. In this situations, one can use a mixture of skew-symmetric distributions to model the population. In this paper we considered the Bayesian approach of regression analysis under the assumption of heterogeneity of population and a skew-symmetric distribution for sub-populations, by using a mixture of skew normal distributions. We used a simulation study and a real world example to assess the proposed Bayesian methodology and to compare it with frequentist approach.

The basic reproduction number is the average number of secondary infection cases generated by a single primary case in a susceptible population. Estimation of the basic reproduction number is important in medical studies. In this paper, we describe a new method for estimating the basic reproduction number by branching processes. Finally, we apply this estimator on real data reported by the National Center for Biotechnology Information in the USA.

This paper presents a new extension of Birnbaum-Saunders distribution based on skew Laplace distribution. Some properties of the new distribution are studied and the EM-type estimators of the parameters with their standard errors are obtained. Finally, we conduct a simulation study and illustrate our distribution by considering two real data example.

The problem of finding tolerance intervals receives very much attention in researches and is widely applied in industry. Tolerance interval is a random interval that covers a proportion of the considered population with a specified confidence level. In this paper, the statistical tolerance limits are expressed for lifetime of k out of n systems with exponentially distributed component lifetimes. Then, we compute the accuracy of proposed tolerance limits and the number of failures needed to attain a desired accuracy level based on type-II right censored data. Finally, we extend our results to the Weibull distribution.

In this paper, the maximum liklihood estimation, unbiased estimations with minimum variance, percentile estimation, best percentile estimation single-observation estimation and the best percentile estimation two-observations in class which are based on order statistics are calculated in two sections for probability density and cumulative distribution functions of the beta Weibull geometric distribution, specially with bathtub-shaped and unimodal failure rate which are useful for modeling of data related to reliability and lifetime. Furthermore, through the simulation method of Monte Carlo and calculation of average square of errors of estimators, they are subjected to comparisons ultimately, the desirable estimator in each section is determined.