Estimation of Gini-index from continuous distribution based on ranked set sampling

This paper introduces the idea of using the novel ranked set sampling scheme for estimating the Gini index from continuous distributions. A one dimensional integral estimation problem based on ranked samples was discussed. It is demonstrated by a simple Monte Carlo experiment that this approach provides an unbiased and more efficient Gini index estimators than the traditional estimators based on simple random sampling

A comparative study for estimating the parameters of the second order moving average process

EnMoving Average process is a representation of a time series written as a finite linear combination of uncorrelated random variables. Our main interest is to compare a classical estimation method; namely Exact Maximum Likelihood Estimation (EMLE) with the Generalized Maximum Entropy (GME) approach for estimating the parameters of the second order moving average processes. In this paper, in applying EMLE we have to find the exact likelihood function through deriving the probability density function of the series. Differentiating the function with respect to the parameters, we can obtain the exact maximum likelihood estimates. On the other hand, the idea of GME is to write the unknown parameters and error terms as the expected value of some proper probability distributions defined over some supports. We carry a simulation study to compare between the presented estimation techniques

STATISTICAL MODELS UTILIZING DEPENDENCE BETWEEN VARIABLES

We propose a model particularly suitable for modeling the relationship between a dependent variable and a vector of independent variables. Binary response variable and categorical data are of a specific interest in this research. I-projection problems arise in wide variety of problems and different settings. It plays a key role in the information theoretic approach to statistics (Kullback,1959 and Good, 1963), e.g. maximization of entropy (Rao 1965, and Jayens,1957) and the theory of large deviations (Sanov, 1957). Maximum likelihood estimation in log-linear models for multinomial distributions is equivalent to solving an I-projection problem (Robertson et. al, 1988). I-projection and Fenchel\u27s duality were used in our research to introduce the proposed model. We consider estimating the parameters of a joint distribution of d random variables by projecting the distribution from the independent case to a convex set C defined to be the intersection of convex cones of probability distributions describing dependence. The goal here is to find the the closest probability distribution in C to the independent probability distribution (vector). The primal problem and its dual are introduced using Fenchel\u27s duality theorem, furthermore the solution of the primal problem is a function of the solution of the dual problem. An algorithm to find the solution is established in the case of two predictor variables and generalized to the case of any d predictor variables, this algorithm is modified from the works of Csiszar (1975), Dykstra (1985), Bhattacharya and Dykstra (1997). Lue and Tsai (2012) proposed a semi-parametric proportional likelihood ratio model which is particularly suitable for modeling a nonlinear monotonic relationship between the outcome variable and a covariate. This model extends the generalized linear model by leaving the distribution unspecified. A theorem that characterizes the solution of the maximization of the log-likelihood function is stated, it was also solved by the profile likelihood approach. An algorithm was established based on the profile likelihood method, but they were not able to find the right conditions to guarantee the convergence of the algorithm. Divergence was not encountered in the real data analysis and simulation study. The logistic regression model is one of the popular statistical models for the analysis of binary data with applications in physical, biomedical, and behavioral sciences, among others. Normally, the asymptotic properties of the maximum likelihood estimates in the model parameters are used for statistical inference. A relationship between the proposed model and logistic regression model is established, and we investigated estimating the proposed model\u27s parameter in perspective of logistic regression. The well-known Newton-Raphson method is studied extensively; an algorithm of the solution is presented. A simulation study is carried out to compare the performance of the Projection method versus the well-known Newton-Raphson method, also the robustness of the two methods is studied, by comparing the two methods when the observed samples are contaminated, and also when the model is misspecified

A comparative study for estimating the parameters of the second order moving average process

EnMoving Average process is a representation of a time series written as a finite linear combination of uncorrelated random variables. Our main interest is to compare a classical estimation method; namely Exact Maximum Likelihood Estimation (EMLE) with the Generalized Maximum Entropy (GME) approach for estimating the parameters of the second order moving average processes. In this paper, in applying EMLE we have to find the exact likelihood function through deriving the probability density function of the series. Differentiating the function with respect to the parameters, we can obtain the exact maximum likelihood estimates. On the other hand, the idea of GME is to write the unknown parameters and error terms as the expected value of some proper probability distributions defined over some supports. We carry a simulation study to compare between the presented estimation techniques

Preparation and Evaluation of Veterinary 0.1% Injectable Solution of Atropine Sulphate

This study introduces the know-how of preparing a multiple injection form atropine sulphate solution. An injectable aqueous solution of atropine sulphate at a concentration of 0.1%. was prepared under aseptic conditions in dark glass bottles each containing 50 ml. The preparation was intended for animal use only. It contained 1g atropine sulphate, 9 g sodium chloride as a normal saline, benzyl alcohol 15 ml as a preservative and water for injection up to 1000 ml. The pH of the solution was adjusted to 4.2 (range 3.0-6.5). The preparation of 0.1% atropine sulphate solution was clear colorless solution free from undesired particles. It complied with the requirements for injectable solutions. Further, the preparation was safe when used under laboratory conditions in chicks, rats and donkeys. It was also effective in preventing dichlorvos (an organophosphate insecticide)-induced poisoning in chicks in a manner comparable to a commercial preparation of 0.1% atropine sulphate. In conclusion, the know-how of a preparation of 0.1% atropine sulphate solution is presented for veterinary use. [Vet. World 2012; 5(3.000): 145-149

Fabrication of a highly flexible low-cost H2 gas sensor using ZnO nanorods grown on an ultra-thin nylon substrate

A “highly flexible low-cost” H2 gas sensor was fabricated via inclined and vertically well-aligned ZnO nanorods on a “cheap, thin (15 µm), and highly flexible” nylon substrate using the hydrothermal method. Morphological, crystallinity, and optical properties of the prepared ZnO nanorods were studied by field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray analysis, X-ray diffraction, and photoluminescence measurements. Results revealed the formation of aligned hexagonal-like nanorods with high aspect ratio and density. The results confirmed the formation of würtzite ZnO phase with a preferred orientation along the (002) direction with high crystallinity, excellent quality, and few defects. The sensitivity and response time behaviors of the ZnO-based gas sensor to hydrogen gas at different operation temperatures and in various hydrogen concentrations were investigated. Under 500 ppm of H2 exposure at different temperatures from room temperature to 180 °C, the sensitivity increased from 109 to 264 %. When the exposed H2 gas increased from 750 to 2000 ppm at a fixed temperature of 75 °C, the sensitivity also sharply increased from 246 to 578 %. Moreover, both the response and recovery time of the device during both tests were enhanced. The hydrogen gas sensing mechanisms of ZnO nanorods in low and high operation temperatures were discussed

The effective and sustainable application of a green amino acid-based corrosion Inhibitor for Cu metal

The human body is a remarkably aggressive medium and applied materials must be extremely resistant to corrosion and destruction, so copper, which is a biological material and is widely used in medicine, is being considered. Copper corrosion may be associated with inflammation in body because it releases ions that are toxic to humans. In aggressive solutions, biomolecules called amino acids act as corrosion inhibitors. In the present research, the purpose is to study the inhibitory action of l-alanine (L-Ala) and l-Leucine (L-Leu) amino acids on the corrosion of Cu. By density functional theory (DFT), inhibition behavior of l-Ala-and l-Leu-and their conformers against Cu corrosion have been investigated. According to values of back-donation of electrons (Eback-d), fraction of electron transferred (ΔN), electrophilicity (ω), electronegativity (χ), softness (σ), hardness (η), energy gap (Eg), ELUMO, and EHOMO computed reactivity factors between copper surface and inhibitors are studied. l-Leu's theoretical indicators confirm its tendency towards adsorption. l-Leu-activity against corrosion has been investigated and favorable results have been obtained

Impact of the International Nosocomial Infection Control Consortium (INICC)’s multidimensional approach on rates of ventilator-associated pneumonia in intensive care units in 22 hospitals of 14 cities of the Kingdom of Saudi Arabia

To analyze the impact of the International Nosocomial Infection Control Consortium (INICC) Multidimensional Approach (IMA) and use of INICC Surveillance Online System (ISOS) on ventilator-associated pneumonia (VAP) rates in Saudi Arabia from September 2013 to February 2017. A multicenter, prospective, before–after surveillance study on 14,961 patients in 37 intensive care units (ICUs) of 22 hospitals. During baseline, we performed outcome surveillance of VAP applying the definitions of the CDC/NHSN. During intervention, we implemented the IMA and the ISOS, which included: (1) a bundle of infection prevention practice interventions, (2) education, (3) outcome surveillance, (4) process surveillance, (5) feedback on VAP rates and consequences and (6) performance feedback of process surveillance. Bivariate and multivariate regression analyses were performed using generalized linear mixed models to estimate the effect of intervention. The baseline rate of 7.84 VAPs per 1000 mechanical-ventilator (MV)-days―with 20,927 MV-days and 164 VAPs―, was reduced to 4.74 VAPs per 1000 MV-days―with 118,929 MV-days and 771 VAPs―, accounting for a 39% rate reduction (IDR 0.61; 95% CI 0.5–0.7; P 0.001). Implementing the IMA was associated with significant reductions in VAP rates in ICUs of Saudi Arabia