An experiment was carried out in the fields of Agriculture College-Baghdad University during spring and autumn of 2015 by using a randomized complete blocks design with three replications. The first season hybridization was established among three pure cultivars of cowpea (Vigna uniguiculata L.) which: Ramshorn, California black eye and Rahawya in full diallel crosses according to Griffing with first method and fixed model (3 parents+ 3 diallel hybrids +3 reciprocal hybrids) and a comparison experiment was in autumn season. The result of statistical analysis showed that there was a significant difference among the parents and their hybrids for all the studied characters. The parent 1 was the higher for root nodules number , leaf number, pod number average pod weight , and pod yield per plant .The diallel cross 1*2 was the higher to pod number 33.27 pod/plant and 2x3 to average pod weight 8.10 g pod-1 and the reciprocal cross 2*1 was higher for green pod yield 285.40 g plant-1. The values of average degree of dominance was 0.873 cm/plant for plant height in diallel crosses while it was more than one for plant height and pod weight average in reciprocal crosses. The heritability in broad sense was high to all characters while in narrow sense it was high to plant height in diallel crosses 68.78% while it's values were high to moderate for most the traits in reciprocal crosses.
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In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). T
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Transforming the common normal distribution through the generated Kummer Beta model to the Kummer Beta Generalized Normal Distribution (KBGND) had been achieved. Then, estimating the distribution parameters and hazard function using the MLE method, and improving these estimations by employing the genetic algorithm. Simulation is used by assuming a number of models and different sample sizes. The main finding was that the common maximum likelihood (MLE) method is the best in estimating the parameters of the Kummer Beta Generalized Normal Distribution (KBGND) compared to the common maximum likelihood according to Mean Squares Error (MSE) and Mean squares Error Integral (IMSE) criteria in estimating the hazard function. While the pr
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This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different
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Often times, especially in practical applications, it is difficult to obtain data that is not tainted by a problem that may be related to the inconsistency of the variance of error or any other problem that impedes the use of the usual methods represented by the method of the ordinary least squares (OLS), To find the capabilities of the features of the multiple linear models, This is why many statisticians resort to the use of estimates by immune methods Especially with the presence of outliers, as well as the problem of error Variance instability, Two methods of horsepower were adopted, they are the robust weighted least square(RWLS)& the two-step robust weighted least square method(TSRWLS), and their performance was verifie
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The experiments were conducted in laboratory conditions of a temperature of 25± 2C and relative moisture of 40± 5 % to evaluate the effectiveness of the cinnamon, lavender and clove essential oils on some biological life aspects of cowpea beetle, C. maculates. Results of the obligative experiment for the effect of the oils on insect adult killing showed that the concentration of 5% caused a mortality percentage averaged 13.33% of the insect males. The mortality percentage of the insect females was 11.3% for the cinnamon and lavender oils. The lavender oil had the lowest effect on adult killing, not exceeding 0. For the effect of the oils on egg laying, clove oil affected the number of eggs highly at the concentration of 5%, result
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The experiments were conducted in laboratory conditions of a temperature of 25± 2C and relative moisture of 40± 5 % to evaluate the effectiveness of the cinnamon, lavender and clove essential oils on some biological life aspects of cowpea beetle, C. maculates. Results of the obligative experiment for the effect of the oils on insect adult killing showed that the concentration of 5% caused a mortality percentage averaged 13.33% of the insect males. The mortality percentage of the insect females was 11.3% for the cinnamon and lavender oils. The lavender oil had the lowest effect on adult killing, not exceeding 0. For the effect of the oils on egg laying, clove oil affected the number of eggs highly at the concentration of 5%, result
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Abstract
Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model
In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe
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In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson†and the “Expectation-Maximization†techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function i
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