Purpose: The research aims to estimate models representing phenomena that follow the logic of circular (angular) data, accounting for the 24-hour periodicity in measurement. Theoretical framework: The regression model is developed to account for the periodic nature of the circular scale, considering the periodicity in the dependent variable y, the explanatory variables x, or both. Design/methodology/approach: Two estimation methods were applied: a parametric model, represented by the Simple Circular Regression (SCR) model, and a nonparametric model, represented by the Nadaraya-Watson Circular Regression (NW) model. The analysis used real data from 50 patients at Al-Kindi Teaching Hospital in Baghdad. Findings: The Mean Circular Error (MCE) criterion was used to compare the two models, leading to the conclusion that the Nadaraya-Watson (NW) circular model outperformed the parametric model in estimating the parameters of the circular regression model. Research, Practical & Social Implications: The recommendation emphasized using the Nadaraya-Watson nonparametric smoothing method to capture the nonlinearity in the data. Originality/value: The results indicated that the Nadaraya-Watson circular model (NW) outperformed the parametric model. Paper type Research paper.
Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the
... Show MoreThe main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
... Show MoreAbstract. Full-waveform airborne laser scanning data has shown its potential to enhance available segmentation and classification approaches through the additional information it can provide. However, this additional information is unable to directly provide a valid physical representation of surface features due to many variables affecting the backscattered energy during travel between the sensor and the target. Effectively, this delivers a mis-match between signals from overlapping flightlines. Therefore direct use of this information is not recommended without the adoption of a comprehensive radiometric calibration strategy that accounts for all these effects. This paper presents a practical and reliable radiometric calibration r
... Show Moreadvancing age. The risk of cardiovascular disease in adults is lowest with systolic BP <120 mm Hg and diastolic BP < 80 mm Hg and increases progressively with higher systolic and diastolic blood pressures. Acupuncture (from the Latin word acus, “needle”, and pungere, meaning “prick”) is a technique in which the practitioner inserts fine needles into specific points on the patient’s body. Accupuncture could be used as a nonpharmacological way of controlling high readings of blood pressure among cases with essential hypertension. This study aimed to investigate the effects of acupuncture on essential hypertension.
Patients and Methods: 51 hypertensive patients of both genders and 30 normotensive control subjects were inv
Missing data is one of the problems that may occur in regression models. This problem is usually handled by deletion mechanism available in statistical software. This method reduces statistical inference values because deletion affects sample size. In this paper, Expectation Maximization algorithm (EM), Multicycle-Expectation-Conditional Maximization algorithm (MC-ECM), Expectation-Conditional Maximization Either (ECME), and Recurrent Neural Networks (RNN) are used to estimate multiple regression models when explanatory variables have some missing values. Experimental dataset were generated using Visual Basic programming language with missing values of explanatory variables according to a missing mechanism at random general pattern and s
... Show MoreThe study of entry and reentry dynamics for space vehicles is very important, particularly for manned vehicles and vehicles which is carry important devices and which can be used again. There are three types for entry dynamic, ballistics entry, glide entry and skip entry. The skip entry is used in this work for describing entry dynamics and determining trajectory. The inertia coordinate system is used to derive equations of motion and determines initial condition for skip entry. The velocity and drag force for entry vehicle, where generate it during entry into earth’s atmosphere are calculated in this work. Also the deceleration during descending and determining entry angles, velocities ratio and altitude ratio have been studied. The c
... Show MoreThe problem of the study and its significance:
Due to the increasing pressures of life continually, and constant quest behind materialism necessary and frustrations that confront us daily in general, the greater the emergence of a number of cases of disease organic roots psychological causing them because of severity of a lack of response to conventional treatments (drugs), and this is creating in patients a number of emotional disorders resulting from concern the risk of disease
That is interested psychologists and doctors searchin
... Show MoreThe purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
... Show MoreBy driven the moment estimator of ARMA (1, 1) and by using the simulation some important notice are founded, From the more notice conclusions that the relation between the sign and moment estimator for ARMA (1, 1) model that is: when the sign is positive means the root gives invertible model and when the sign is negative means the root gives invertible model. An alternative method has been suggested for ARMA (0, 1) model can be suitable when