The optimal criteria are used to find the optimal design in the studied model. These kinds of models are included the paired comparison models. In these models, the optimal criteria (D-optimality) determine the optimal paired comparison. In this paper, in addition to introducing the quadratic regression model with random effects, the paired comparison models were presented and the optimal design has been calculated for them.

Value-at-Risk and Average Value-at-Risk are tow important risk measures based on statistical methoeds that used to measure the market's risk with quantity structure. Recently, linear regression models such as least squares and quantile methods are introduced to estimate these risk measures. In this paper, these two risk measures are estimated by using omposite quantile regression. To evaluate the performance of the proposed model with the other models, a simulation study was conducted and at the end, applications to real data set from Iran's stock market are illustarted.

The presence of endogenous variables in the statistical models leads to inconsistent and bias estimators for the parameters. In this case, several approaches have been proposed which are able to tackle the biase and inconsistency problems only in large sample situations. One of these methods is biased on instrumental variables which causes removing endogenous variables. The method of two-stage least squares is another approach in this case that it has more accurate than ordinary least squares. This paper aims to enhance the accuracy of three methods of estimation based upon least square methodology called, two-stage iterative least squares, two-stage Jackknife least squares and also two-stage calibration least squares. In order to evaluate the performance of each method, a simulation study is conducted. Also, using data collected in 1390 related to the cost and revenue in Iran, those methods to estimate parameters are compared.

Most of the research of design optimality is conducted on linear and generalized linear models. In applicable studies, in agriculture, social sciences, etc, usually in addition to fixed effects, there is also at least one random effect in the model. These models are known as mixed models. In this article, Beta regression model with a random intercept is considered as a mixed model and locally D-optimal design is calculated for simple and quadratic forms of the model and the trend of changes of optimal design points for different parameter values will be studied. For the simple model, a two point locally D-optimal design has been obtained for different parameter values and in the quadratic model, a three point locally D-optimal design has been acquired. Also, according to the efficiency criterion, these locally D-optimal designs are compared with the same designs. It was observed that the efficiency of optimal design, when the random intercept is not considered in the model is lower than the case in which the random effect is considered.

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 nonparametric and semiparametric regression models have been improved extensively in the field of cross-sectional study and independent data, but their improvement in the field of longitudinal data is restricted to the recent years or decade. Since the common methods for correlated data have a much lower ability rather than for the independent data, we should use the models which consider the correlation among the data. The mixed and marginal models consider the correlation factor among the data, and so obtain a better fit for that. Furthermore, the semiparametric regression has more flexibility compared to the parametric and nonparametric regression. Consequently, based on the properties of the longitudinal data, the marginal longitudinal semiparametric regression with the penalized spline estimations, is a suitable choice for the analysis of the longitudinal data. In this article, the semiparametric regression with different coefficients which specifies the relationship between a response variable and an explanatory variable based on another explanatory variable is assessed. In addition, Bayesian inference on the nonparametric model for a simulated data and the marginal longitudinal semiparametric model for a real data have been done by standard software; and the results have good performance.

The presence of outliers in data set may affect structure of analysis of variance test so that test results led to wrong acceptance or rejection of null hypothesis. In this paper the method of robust permutation distribution of F statistic based on trimmed mean is proposed. This method by permutation distribution of a function of trimmed mean, reduces the sensitivity to classical assumptions such as normality and presence of outlier and it guarantees the reliability of result. The proposed method is compared with robust analysis of variance based of forward search approach. The proposed method, unlike the forward search-based approach is free of restricted parametric assumptions and computationally spend less time. Numerically assessment results on type I error and power of test, demonstrate good performance of this robust method in comparison with competitor method.

According to multiple sources of errors, shape data are often prone to measurement error. Ignoring such error, if does exists, causes many problems including the biasedness of the estimators. The estimators coming from variables without including the measurement errors are called naive estimators. These for rotation and scale parameters are biased, while using the Procrustes matching for two dimensional shape data. To correct this and to improve the naive estimators, regression calibration methods that can be obtained through the complex regression models and invoking the complex normal distribution, as well as the conditional score are proposed in this paper. Moreover, their performance are studied in simulation studies. Also, the statistical shape analysis of the sand hills in Ardestan in Iran is undertaken in presence of measurement errors.

In this paper, it is shown that using the cliams with Pareto distribution for computing the ruin probabilities could has detriment for the heads of insurance company. With computing the relative error of these cliams it is shown that the estimation of claims mean is not suitable in insurance models. We will show that existance of claims with Pareto distribution in the excess of loss reinsurance model may be detriment for the policyholders of company. Also in this portfolio, with computing the conditional expectation of claims measure show that using the claims with Pareto distribution is not suitable in the estimation of claims. The estimation of conditional expectation of random variable of claims is computed by simulation method for some of the statistical distributions. The results are investigated with real examples.

Variances homogeneity test are mostly applied as a preliminary test to other analyses like test of equality of means. So far, several tests have been offered in randomized complete block design, that the most prevalent of them are Bartlett and Levene tests, and others are generalized kind of these two tests. The distribution of statistics for these tests are obtained asymptotically. Recently, a test has been introduced base on estimated critical values. In this paper, nine tests are examined based on estimated critical values method and their performance are evaluated with various blocks and treatment groups for normal and t-student distributions by a simulation study. The method of estimated critical values has a good performance in the type I error and a power improvement with respect to using asymptotic distribution.

The study of regression diagnostic, including identification of the influential observations and outliers, is of particular importance. The sensitivity of least squares estimators to the outliers and influential observations lead to extending the regression diagnostic in order to provide criteria to assess the anomalous observations. Detecting influential observations and outliers in the presence of collinearity is a complicated task, in the sense that collinearity may cover some of the unusual data. One of the considerable methods to identify outliers is the mean shift outliers method. In this article, we extend the mean shift outliers method to the ridge estimates under linear stochastic restrictions, which is used to reduce the effect of collinearity, and to provide the test statistic to identify the outliers in these estimators. Finally, we show the ability of our proposed method using a practical example of real data.

One of the applicable tools, in order to develop the economy's politics, is Iranian's cooperation in increasing their level of public knowledge and the humanization of economic. Economical index, rate, price, and percentage are not informative only. From this point of view, one of the scientific ways to study the economic data is "Statistical Modeling" through the applicable concept of "Copula Function". In this paper, through the copula functions and the applicable concept of dependence, called "Directional dependence", the dependence structure between variations in family's income and the expenses allocated to buy cultural and miscellaneous goods would be widely studied. Simulation results show that by decreasing the level of income, Iranian families tend to decrease their cultural costs rather than unnecessary miscellaneous costs.