In this article, performing and deriving te probability density function for Rayleigh distribution is done by using ordinary least squares estimator method and Rank set estimator method. Then creating interval for scale parameter of Rayleigh distribution. Anew method using   is used for fuzzy scale parameter. After that creating the survival and hazard functions for two ranking functions are conducted to show which one is beast.

In this research، a comparison has been made between the robust estimators of (M) for the Cubic Smoothing Splines technique، to avoid the problem of abnormality in data or contamination of error، and the traditional estimation method of Cubic Smoothing Splines technique by using two criteria of differentiation which are (MADE، WASE) for different sample sizes and disparity levels to estimate the chronologically different coefficients functions for the balanced longitudinal data which are characterized by observations obtained through (n) from the independent subjects، each one of them is measured repeatedly by group of  specific time points (m)،since the frequent measurements within the subjects are almost connected an

n this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used: local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the types of the kernel boundary func

Background: Dialysis is in common use to treat patients
with end stage renal failure .However longstanding dialysis
harboring some cellular changes in various body fluids.
This study was conducted in order to detect these changes
in urine.
Objective: The study was conducted to detect cellular
changes in urine of patients with longstanding dialysis.
Method: Fifty-three urine samples were examined
cytologically obtained from patients with longstanding
dialysis during 6 months period. Freshly voided midstream
urine samples were taken . Samples were centrifuged and 2
to 3 drops of sediments were smeared on 2 glass slides and
fixed in 95% ethyl alcohol then stained with Hand E stain
to be evaluated.
R

This paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data.

Resource estimation is an essential part of reservoir evaluation and development planning which highly affects the decision-making process. The available conventional logs for 30 wells in Nasiriyah oilfield were used in this study to model the petrophysical properties of the reservoir and produce a 3D static geological reservoir model that mimics petrophysical properties distribution to estimate the stock tank oil originally in place (STOOIP) for Mishrif reservoir by volumetric method. Computer processed porosity and water saturation and a structural 2D map were utilized to construct the model which was discretized by 537840 grid blocks. These properties were distributed in 3D Space using sequential Gaussian simulation and the variation in

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

Abstract

In this research we study the wavelet characteristics for the important time series known as Sunspot, on the aim of verifying the periodogram that other researchers had reached by the spectral transform, and noticing the variation in the period length on one side and the shifting on another.

A continuous wavelet analysis is done for this series and the periodogram in it is marked primarily. for more accuracy, the series is partitioned to its the approximate and the details components to five levels, filtering these components by using fixed threshold on one time and independent threshold on another, finding the noise series which represents the difference between

 In this paper, we estimate the survival function for the patients of lung cancer using different nonparametric estimation methods depending on sample from complete real data which describe the duration of survivor for patients who suffer from the lung cancer based on diagnosis of disease or the enter of patients in a hospital for period of two years (starting with 2012 to the end of 2013). Comparisons between the mentioned estimation methods has been performed using statistical indicator mean squares error, concluding that the survival function for the lung cancer by using shrinkage method is the best

As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detec