This paper presents an assessment of the small-sample performance of the three well-known estimators of components variance in random effects model for panel data. The estimators considered are Swamy-Arora, Wansbeek-Kaptayn and Wallace-Hussain. To this end, by simulating a one-way error component model in the form of random effects, small sample performance of three variance estimators is studied. The implications of these results for indentifying the model and its estimation are specified.In these simulations, conditions under which Swamy-Arora estimator is inferior to alternatives are expressed. It is shown that in small samples the estimator thus obtained can give highly wrong guidance. In one-way error component model this small sample size refers to the number of cross-sections.

To conduct any breeding program, understanding of the genetic structure of traits and effect of environment and genetic by environment interaction as well as effects of random or fixed in the analysis of results is essential. Subsequently, analysis of variances and variance components are important in plant and animal breeding. The ANOVA is one of the best estimators for variance components. But this estimator is not preferred to maximum likelihood (ML) and Restricted Maximum Likelihood (REML) methods when variance components are negatively estimated and unbalanced datasets arise. Therefore, the objective of this research is a review of comparison of estimates of variance components using ANOVA, ML and REML method in linear mixed models using experimental data.

The purpose of the present study was to estimation of (co)variance components and genetic parameters of the growth curve in crossbred population. For this purpose, body weight records were fitted through the Gompertz curve and curve parameters were estimated. The final weight (Wf), hatch weight (W0), mature rate index (K), age (ti) and weight (Wi) in inflection point and growth rate in different days for all of the birds were predicted by Gompertz model. The estimated of (co)variance and genetic parameters were performed using a multi-trait animal model through Gibbs sampling. Heritability of the growth cure parameters including final weight, hatch weight, mature index and weight and age in inflection point was 0. 335, 0. 269, 0. 273, 0. 291 and 0. 397, respectively. Also, the heritability of growth rate traits from hatch to 45 days was estimated high and varied between 0. 311 to 0. 424. The genetic correlation between hatch weight with mature index and final weight was positive (0. 14 and 0. 24), but the genetic correlation between hatch weight with age and weight at inflection point was negative (-0. 24 and-0. 12). The genetic correlation of the mature index with hatch weight, mature weight, age and weight at inflection point was negative. Genetic correlation of the mature index with growth rate in the early ages of bird was positive and it was estimated medium and negative for late. The weight at inflection point had the positive genetic correlation with most of growth rate traits and the change trend from beginning to end of growth period was ascending.

Knowing the components of economic education provides the dynamic background of education in this field. The aim of the current research is to investigate the indicators and components of the economic education curriculum. The approach of the current research is qualitative and its method is research synthesis. The research community is all the articles (211 articles) that have been presented from 1390 to 1401 AD and from 2010 to 2022 AD regarding economic education in curricula. The sample of the research is 32 articles, which were selected based on thematic monitoring, theoretical data saturation, and purposefully. The research data were collected from the qualitative analysis of the studied articles. By analyzing the data, the dimensions of vitality in the curriculum of the elementary school in 4 factors and 55 categories including the dimension of individual factors (including indicators and components related to personality characteristics); The dimension of culture and economic ethics (economic fields in the society); The dimension of economic concepts and knowledge (indices in economic education) and the dimension of collective and communication concepts (communication and interaction indicators in education) were classified. Economic education is dependent on internal and external components in educational systems, which requires macro-planning in this field.

Survey implementation is one of the common methods to data collection. To determine quality of survey results, survey errors should be studied. Response error is an essential part of nonsampling errors and modeling of response error has many applications. One of the applications is to estimate precision of survey results. In this paper, two methods of variance components estimation of the response error model is studied and compared. In this purpose, estimators of variance components is computed by Maximum Likelihood and ANOVA methods. As an application of the presented maximum likelihood estimators, variance components is estimated for some data sets and optimization is applied to assess validity of the proposed estimators. These calculations show that nonlinear optimization confirms the presented maximum likelihood estimators, in all of the data sets. Furthermore, Mean Squares Error criterion is applied to compare the presented maximum likelihood estimators with ANOVA estimators of variance components of the response error model. In the other words, variance and bias of the estimators is calculated to compare two methods of estimation.

The purpose of this study is to test the Fama and French three-factor model in Tehran Stock Exchange. In order to do this, six portfolios including 616 stocks were formed for the years 2004-2009, regarding the size and the book values to the market value ratio. The results indicated that the beta, the size and the book value to market value ratio had significant effects on portfolio return. Adding two factors of the size of the company and the book value to market value ratio to the single factor CAMP resulted in increase in the coefficient of determination. This implies that the three-factor model describes variance variability of the percentage of portfolio return more than CAMP. Two factors of the size and the book value to market value ratio describe variance variability of the portfolio return considerably and their coefficients are statistically significant.

Introduction: Traumatic brain injury (TBI) is one of the leading causes of death globally and one of the most important diseases indicated by the World Health Organization (WHO). Several studies have concluded that brain damage can dramatically increase functional connectivity (FC) in the brain. The effects of this hyper-connectivity are not yet fully understood and are being studied by neuroscientists. Accordingly, this study identifies areas of the brain where, after brain injury, an acute increase in FC in such areas is observed. Methods: The data used in this study were downloaded from the accessible open functional magnetic resonance imaging (fMRI) site. The data included fMRI of 14 patients with severe TBI and 12 healthy individuals. The longitudinal model of variance components investigated the difference between FC in the baseline effect and the longitudinal trend between the TBI and control groups. Results: After fitting the longitudinal model of variance components, no difference was observed between the FC of the two groups due to the baseline effect. However, in the longitudinal trend of FC, there was a statistically significant difference between the three pairs of cerebellum left, cerebellum right, superior frontal gyrus left, superior frontal gyrus right, thalamus left, and thalamus right in the TBI group compared to the control group. Conclusion: The results showed that FC was sharply increased in 3 pairs of areas in people with TBI. This hyper-connectivity can affect individuals' cognitive functions, including motor and sensory functions. The exact extent of this effect is unclear and requires further investigation by neuroscientists.

Fluctuations in travel demand and uncertainty in transportation supply make travel time uncertain. Uncertainty (variability) in travel time can be attributed to three main components including vehicle-to-vehicle variability, period-to-period variability and day-to-day variability. Several studies developed indexes to measure travel time variability. However, no methods have been developed to estimate the contribution of the main components of variability. In this paper, we employ the analysis of variance method to determine the relative contributions of the aforementioned components to the total variability in travel time. To examine the applicability of the proposed method, real travel time data collected over 3 months is used. The results of the present study show that all three components play a significant role in the variability of travel time. Based on the travel time data used in this study, the share of vehicle-to-vehicle, period-to-period and day-to-day variability components are 15.8, 36.1, and 22.3 percent, respectively. Moreover, 25.8 percent of total variability is contributed to the interaction of day-to-day and period-to-period components. In addition, according to the results of this research, the probability distribution of travel time is multimodal, especially in peak hour periods. Therefore, mixture distributions can model travel time variability more accurately than single distributions.