Incomplete Gamma Distribution: A New Two Parameter Lifetime Distribution with Survival Regression Model

We introduce a new two parameter lifetime distribution constructed via incomplete gamma function which includes exponential distribution as a limiting case. This distribution is more flexible than most of the two parameter extended exponential distributions. Various statistical properties such as moments, moment generating function and certain useful characterizations based on the ratio of two truncated moments are presented. Maximum likelihood estimation method is used for estimating parameters of this distribution and a survival regression model based on the proposed distribution is presented for fitting breast cancer data set

Fundamental Modeling Exchange Rate using Genetic Algorithm: A Case Study of European Countries

Genetic Algorithms (GAs) are an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this study we apply GAs for Fundamental Models of Exchange Rate Determination in exchange rate market. In this framework, we estimated absolute and relative purchasing power parity, Mundell-Fleming, sticky and flexible prices, equilibrium exchange rate and portfolio balance model as fundamental models for European Union’s Euro against the US Dollar using monthly data from January 1992 to December 2008. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria i.e. R-squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Based on obtained Results, it seems that for explaining of EU Euro against the US Dollar exchange rate behavior, equilibrium exchange rate and portfolio balance model are better than the other fundamental models

Bias-corrected Maximum-Likelihood Estimator for the Parameter of the Logarithmic Series Distribution and its Characterizations

In this article, we study parameter estimation of the logarithmic series distribution. A well-known method of estimation is the maximum likelihood estimate (MLE) and this method for this distribution resulted in a biased estimator for the small sample size datasets. The goal here is to reduce the bias and root mean square error of MLE of the unknown parameter. Employing the Cox and Snell method, a closed-form expression for the bias-reduction of the maximum likelihood estimator of the parameter is obtained. Moreover, the parametric Bootstrap bias correction of the maximum likelihood estimator is studied. The performance of the proposed estimators is investigated via Monte Carlo simulation studies. The numerical results show that the analytical bias-corrected estimator performs better than bootstrapped-based estimator and MLE for small sample sizes. Also, certain useful characterizations of this distribution are presented. An example via a real dataset is presented for the illustrative purposes

The impact of foreign direct investment on economic growth: the Portuguese experience

This study examines the link between economic growth and foreign direct investment for Portugal. Using a panel data approach, the results show that there is convergence among Portugal and her trading partners. Our results also demonstrate that foreign direct investment and bilateral trade promote economic growth. The growth is negatively correlated with inflation and the initial level of GDP per capita. As in previous studies taxes plays a minor role on determining the growth

Optimization of the Matching Network for using Genetic Algorithm

Microstrip-like antenna (MLA) which was developed nearly a decade ago, is a powerful radiating element. The primary challenge in designing a MLA is to provide an optimized matching network such that the overall input reflection is kept as low as possible within the required bandwidth. In this paper, the necessity and procedure of applying genetic algorithm to MLA problems has been presented. Comparison with the existing literature shows good agreement in the overall input reflection.Comment: Optimizatio

Optimal Location Design for Prediction of Spatial Correlated Environmental Functional Data

The optimal choice of sites to make spatial prediction is critical for a better understanding of really spatio-temporal data. It is important to obtain the essential spatio-temporal variability of the process in determining optimal design, because these data tend to exhibit both spatial and temporal variability. Two new methods of prediction for spatially correlated functional data are considered. The first method models spatial dependency by fitting variogram to empirical variogram, similar to ordinary kriging (univariate approach). The second method models spatial dependency by linear model co-regionalization (multivariate approach). The variance of prediction method was chosen as the optimization design criterion. An application to CO concentration forecasting was conducted to examine possible differences between the design and the optimal design without considering temporal structure

Investigation of correlation analysis and relationships between grain yield and other quantitative traits in chickpea (Cicer arietinum L.)

Understanding the relationships between chickpea (Cicer arietinum L.) yield and yield components is critical to utilizing these relationships effectively and thus developing desirable varieties. This research was done in order to investigate the correlation analysis and relationships between grain yield and other quantitative traits with three chickpea cultivars (Filip-84-48-c, Ilc-482 and Arman) and three sowing date (March 6, 21 and April 4). A 3 x 3 factorial experiment in randomized complete block design (RCBD) format with three replications was conducted in the research field of the Azad University of Kermanshah, during 2006. The results showed that both sowing date and cultivar had significant effects on grain yield and yield components of chickpea. Early planting chickpea produced the highest plant height, distance of first pod from the earth surface, number of sub branch, number of pods per plant, number of seeds per plant, 100-seed weight, grain yield, biological yield and harvest index. Thesowing done on March 6 had the highest while April 4 had the lowest grain yield. There were significant differences between cultivars of grain yield. The highest grain yield belonged to Arman with 1067.1 kg/ha. Results showed that number of seeds per plant (r = 0.846**), number of pods per plant (r = 0.827**), plant height (r = 0/813**) and biological yield (r = 0.798**) had the highest positive correlation with grain yield. The results of path coefficient analysis revealed that number of seeds per plant had high and positive direct effects (0.76) on seed yield, but number of pods per plant was an important constituent (0.41)

A New Generalized Modified Weibull Distribution

We introduce a new distribution, so called A new generalized modified Weibull (NGMW) distribution. Various structural properties of the distribution are obtained in terms of Meijer’s G–function, such as moments, moment generating function, conditional moments, mean deviations, order statistics and maximum likelihood estimators. The distribution exhibits a wide range of shapes with varying skewness and assumes all possible forms of hazard rate function. The NGMW distribution along with other distributions are fitted to two sets of data, arising in hydrology and in reliability. It is shown that the proposed distribution has a superior performance among the compared distributions as evidenced via goodness–of–fit tests