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Robust Estimator for Semiparametric Generalized Additive Model
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Generalized Additive Model has been considered as a multivariate smoother that appeared recently in Nonparametric Regression Analysis. Thus, this research is devoted to study the mixed situation, i.e. for the phenomena that changes its behaviour from linear (with known functional form) represented in parametric part, to nonlinear (with unknown functional form: here, smoothing spline) represented in nonparametric part of the model. Furthermore, we propose robust semiparametric GAM estimator, which compared with two other existed techniques.

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Publication Date
Sat Apr 01 2023
Journal Name
Full Text Book Of Minar Congress8
REGULARITY VIA PRE- GENERALIZED OPEN SETS
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By use the notions pre-g-closedness and pre-g-openness we have generalized a class of separation axioms in topological spaces. In particular, we presented in this paper new types of regulαrities, which we named ρg­regulαrity and Sρg­regulαrity. Many results and properties of both types have been investigated and have illustrated by examples.

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Publication Date
Sat Apr 04 2020
Journal Name
Journal Of Xi'an University Of Architecture & Technology
Regularity via semi-generalized open set
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In this work the concept of semi-generalized regular topological space was introduced and studied via semi generalized open sets. Many properties and results was investigated and studied, also it was shown that the quotient space of semi-generalized regular topological space is not, in general semi-generalizedspace.

Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
New Normality on Generalized Topological Spaces
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Abstract<p>A space X is named a πp – normal if for each closed set F and each π – closed set F’ in X with F ∩ F’ = ∅, there are p – open sets U and V of X with U ∩ V = ∅ whereas F ⊆ U and F’ ⊆ V. Our work studies and discusses a new kind of normality in generalized topological spaces. We define ϑπp – normal, ϑ–mildly normal, & ϑ–almost normal, ϑp– normal, & ϑ–mildly p–normal, & ϑ–almost p-normal and ϑπ-normal space, and we discuss some of their properties.</p>
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Publication Date
Mon Feb 01 2021
Journal Name
University Of Baghdad, College Of Education For Pure Sciences / Ibn Al-haitham, Department Of Mathematics
Generalized Rough Digraphs and Related Topologies1
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In this thesis, we introduce eight types of topologies on a finite digraphs and state the implication between these topologies. Also we studied some pawlak's concepts and generalization rough set theory, we introduce a new types for approximation rough digraphs depending on supra open digraphs. In addition, we present two various standpoints to define generalized membership relations, and state the implication between it, to classify the digraphs and help for measure exactness and roughness of digraphs. On the other hand, we define several kinds of fuzzy digraphs. We also introduce a topological space, which is induced by reflexive graph and tolerance graphs, such that the graph may be infinite. Furthermore, we offered some properties of th

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Publication Date
Sun Dec 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Use Generalized Pareto Survival Models to Estimation Optimal Survival Time for Myocardial Infarction Patients
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The survival analysis is one of the modern methods of analysis that is based on the fact that the dependent variable represents time until the event concerned in the study. There are many survival models that deal with the impact of explanatory factors on the likelihood of survival, including the models proposed by the world, David Cox, one of the most important and common models of survival, where it consists of two functions, one of which is a parametric function that does not depend on the survival time and the other a nonparametric function that depends on times of survival, which the Cox model is defined as a semi parametric model, The set of parametric models that depend on the time-to-event distribution parameters such as

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Publication Date
Sun Dec 07 2014
Journal Name
Baghdad Science Journal
Convergence of the Generalized Homotopy Perturbation Method for Solving Fractional Order Integro-Differential Equations
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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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Publication Date
Wed Oct 01 2014
Journal Name
Iosr Journal Of Mathematics
Flow through an Oscillating Rectangular Duct for Generalized Oldroyd-B Fluid with Fractional Derivatives
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The analytic solution for the unsteady flow of generalized Oldroyd- B fluid on oscillating rectangular duct is studied. In the absence of the frequency of oscillations, we obtain the problem for the flow of generalized Oldroyd- B fluid in a duct of rectangular cross- section moving parallel to its length. The problem is solved by applying the double finite Fourier sine and discrete Laplace transforms. The solutions for the generalized Maxwell fluids and the ordinary Maxwell fluid appear as limiting cases of the solutions obtained here. Finally, the effect of material parameters on the velocity profile spotlighted by means of the graphical illustrations

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Publication Date
Tue Mar 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
Some Estimation for the Parameters and Hazard Function of Kummer Beta Generalized Normal Distribution
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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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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
Generalized tupled common fixed point theorems for weakly compatible mappings in fuzzy metric space
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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
An Application of Non-additive Measures and Corresponding Integrals in Tourism Management
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Non-additive measures and corresponding integrals originally have been introduced by Choquet in 1953 (1) and independently defined by Sugeno in 1974 (2) in order to extend the classical measure by replacing the additivity property to non-additive property. An important feature of non –additive measures and fuzzy integrals is that they can represent the importance of individual information sources and interactions among them. There are many applications of non-additive measures and fuzzy integrals such as image processing, multi-criteria decision making, information fusion, classification, and pattern recognition. This paper presents a mathematical model for discussing an application of non-additive measures and corresp

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