The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the purposes of assessment and estimating and fitting, this along with the use of the classical method. It was to identify the best estimation method through the use of a of comparison criteria: Root of Mean Square Error: RMSE, and the Mean Absolute Percentage Error: MAPE. Sample sizes were selected as (n = 18, 30, 50, 81) which represents the size of data generation n = 18 five-year age groups for the phenomenon being studied and the sample size n = 81 age group represents a unilateral, and replicated the experiment (500) times. The results showed the simulation that the Maximum Likelihood method is the best in the case of small and medium-sized samples where it was applied to the data for five-year age groups suffering from disturbances and confusion of Iraq Household socio-Economic survey: IHSES II2012 while entropy method outperformed in the case of large samples where it was applied to age groups monounsaturated resulting from the use of mathematical method lead to results based on the staging equation data (Formula for Interpolation) placed Sprague (Sprague) and these transactions or what is called Sprague transactions (Sprague multipliers) are used to derive the preparation of deaths and the preparation of the population by unilateral age within the age groups a five-year given the use of the death toll and the preparation of the population in this age group and its environs from a five-year categories by using Excel program where the use of age groups monounsaturated data for accuracy not detect any age is in danger of annihilation.
Abstract
In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to min
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Gumbel distribution was dealt with great care by researchers and statisticians. There are traditional methods to estimate two parameters of Gumbel distribution known as Maximum Likelihood, the Method of Moments and recently the method of re-sampling called (Jackknife). However, these methods suffer from some mathematical difficulties in solving them analytically. Accordingly, there are other non-traditional methods, like the principle of the nearest neighbors, used in computer science especially, artificial intelligence algorithms, including the genetic algorithm, the artificial neural network algorithm, and others that may to be classified as meta-heuristic methods. Moreover, this principle of nearest neighbors has useful statistical featu
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In this paper, point estimation for parameter ? of Maxwell-Boltzmann distribution has been investigated by using simulation technique, to estimate the parameter by two sections methods; the first section includes Non-Bayesian estimation methods, such as (Maximum Likelihood estimator method, and Moment estimator method), while the second section includes standard Bayesian estimation method, using two different priors (Inverse Chi-Square and Jeffrey) such as (standard Bayes estimator, and Bayes estimator based on Jeffrey's prior). Comparisons among these methods were made by employing mean square error measure. Simulation technique for different sample sizes has been used to compare between these methods.
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In this paper,the homtopy perturbation method (HPM) was applied to obtain the approximate solutions of the fractional order integro-differential equations . The fractional order derivatives and fractional order integral are described in the Caputo and Riemann-Liouville sense respectively. We can easily obtain the solution from convergent the infinite series of HPM . A theorem for convergence and error estimates of the HPM for solving fractional order integro-differential equations was given. Moreover, numerical results show that our theoretical analysis are accurate and the HPM can be considered as a powerful method for solving fractional order integro-diffrential equations.
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Background: Placenta is a chief cause of maternal and perinatal mortality and significant factor in fetal growth retardation. It undergoes different variations in weight, volume, structure, shape and function continuously throughout the gestation tosupport the prenatal life. Cautious examination of placenta can give information which can be useful in the management of complications in mother and the newborn. Objective: The present work has been attempted towards determination of the morphological ( macroscopic and microscopic) parameters of human full-term placentae and their relation with different parity and age group of mothers. Patients and Methods: A whole of 40 placentae were recently collected.They were divided into four groups
... Show MoreThe selection and assessment of single-photon detection modules is a crucial problem in satellite-based QKD systems. The system's overall efficiency, secure key rate and quantum bit error rate are all significantly influenced by single-photon detection modules. There is a knowledge gap about the practical performance of commercially available single-photon detectors because existing research frequently relies on theoretical characteristics. This paper introduces a study on the effect of the parameters of three commercial single photon detection modules from ID Quantique company: ID Qube, ID100, and ID281 on certain Bennett-Brassard 1984 protocol parameters such as secure key rate, mean photon number per pulse, quantum bit error rate
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The aim of the research is to study the comparison between (ARIMA) Auto Regressive Integrated Moving Average and(ANNs) Artificial Neural Networks models and to select the best one for prediction the monthly relative humidity values depending upon the standard errors between estimated and observe values . It has been noted that both can be used for estimation and the best on among is (ANNs) as the values (MAE,RMSE, R2) is )0.036816,0.0466,0.91) respectively for the best formula for model (ARIMA) (6,0,2)(6,0,1) whereas the values of estimates relative to model (ANNs) for the best formula (5,5,1) is (0.0109, 0.0139 ,0.991) respectively. so that model (ANNs) is superior than (ARIMA) in a such evaluation.
This study aims to isolate the pathogenic yeasts from genital tract and investigate their relationship with the age .The results clarified that the most pathogenic yeast isolated from genital tract was Candida albicans , also the results of C.albicanas isolates susceptibility test, to different antifungal revealed that they were sensitive to Miconazole, Ketoconazole and Clotrimazol and were resistant to Nystatin and Grisofulvin. The study of relationship of vaginal infection with the age showed that the incidence of infection with Candida was high among females age group (19-39 years).
The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables. The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.
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Use of lower squares and restricted boxes
In the estimation of the first-order self-regression parameter
AR (1) (simulation study)