scholarly journals Credibility Weighted Hazard Estimation

2000 ◽  
Vol 30 (2) ◽  
pp. 405-417 ◽  
Author(s):  
Jens Perch Nielsen ◽  
Bjørn Lunding Sandqvist
Keyword(s):  
Hilbert Space ◽  
Kernel Method ◽  
Sparse Data ◽  
Hazard Curve ◽  
Credibility Estimator ◽  
Space Projection ◽  
Hazard Estimation ◽  
Available Information ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Method

AbstractCredibility weighting is helpful in many insurance applications where sparse data crave information from other sources of data. In this paper we aim at estimating a hazard curve using the nonparametric kernel method, where a credibility weighting principle is used locally, so that areas of sparse data for one subgroup can be alleviated by available information from other subgroups. The credibility estimator is found through a Hilbert space projection formulation of Buhlmann-Straub's credibility approach.

Nonparametric Kernel Method to Hedge Downside Risk

2019 ◽  
Vol 19 (4) ◽  
pp. 929-944
Author(s):  
Jinbo Huang ◽  
Ashley Ding ◽  
Yong Li
Keyword(s):  
Kernel Method ◽  
Downside Risk ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Method

scholarly journals A Novel Method for Solving KdV Equation Based on Reproducing Kernel Hilbert Space Method

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Mustafa Inc ◽  
Ali Akgül ◽  
Adem Kiliçman
Keyword(s):  
Hilbert Space ◽  
Reproducing Kernel ◽  
Kernel Method ◽  
Kdv Equation ◽  
Reproducing Kernel Hilbert Space ◽  
Reproducing Kernel Method ◽  
Numerical Examples ◽  
The Kdv Equation ◽  
Novel Method ◽  
Reproducing Kernel Theory

We propose a reproducing kernel method for solving the KdV equation with initial condition based on the reproducing kernel theory. The exact solution is represented in the form of series in the reproducing kernel Hilbert space. Some numerical examples have also been studied to demonstrate the accuracy of the present method. Results of numerical examples show that the presented method is effective.

scholarly journals Bayesian Kernel Methods

2021 ◽  
Vol 6 (1) ◽  
pp. 26-39
Author(s):  
Arti Saxena ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Medical Decision Making ◽  
Kernel Method ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Learning Tools ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Available Information

In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients, behaviour of the diseases, etc. are collected, and now it becomes the job of the practitioner at source to use the available data for diagnosing the diseases accurately and then prescribe the relevant treatment. Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. The inharmonious behavior of the data is the motivation behind the development of new tools and demonstrates the available information to some meaningful information for decision making. As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed, which suits the purpose of researchers in the enhancement of their research in the domain of medical decision making.

Verification of Optimality and Costate Estimation Using Hilbert Space Projection

2009 ◽  
Vol 32 (4) ◽  
pp. 1345-1355 ◽  
Author(s):  
Baljeet Singh ◽  
Raktim Bhattacharya ◽  
Srinivas R. Vadali
Keyword(s):  
Hilbert Space ◽  
Space Projection

scholarly journals A REPRODUCING KERNEL METHOD FOR SOLVING A CLASS OF NONLINEAR SYSTEMS OF PDES

2014 ◽  
Vol 19 (2) ◽  
pp. 180-198 ◽  
Author(s):  
Maryam Mohammadi ◽  
Reza Mokhtari
Keyword(s):  
Hilbert Space ◽  
Nonlinear Systems ◽  
System Performance ◽  
Reproducing Kernel ◽  
Kernel Method ◽  
Reproducing Kernel Hilbert Space ◽  
Coupled System ◽  
Reproducing Kernel Method ◽  
Nonlinear Hyperbolic System ◽  
Systems Of Pdes

This paper is concerned with a technique for solving a class of nonlinear systems of partial differential equations (PDEs) in the reproducing kernel Hilbert space. The analytical solution is represented in the form of series. An iterative method is given to obtain the approximate solution. The convergence analysis is established theoretically. The proposed method is successfully used for solving a coupled system of viscous Burgers’ equations and a nonlinear hyperbolic system. Performance of the method is tested in terms of various error norms. In the case of non-availability of exact solution, it is compared with the existing methods.

scholarly journals Asymptotic Properties for Weighted Averages of Longitudinal Dependent Data

2018 ◽  
Vol 7 (6) ◽  
pp. 100
Author(s):  
Brahima Soro ◽  
Ouagnina Hili ◽  
Youssouf Diagana
Keyword(s):  
Longitudinal Data ◽  
Covariance Function ◽  
Kernel Method ◽  
Asymptotic Properties ◽  
Dependent Data ◽  
Independent Data ◽  
Function Estimation ◽  
Mixing Conditions ◽  
Weighted Averages ◽  
Nonparametric Kernel

This paper presents a set of normality general results for kernel weighted averages. We extend existing literature for independent data (Yao, 2007) to stationary dependent longitudinal data. The asymptotic properties of proposed weighted averages are investigate under α-mixing conditions. These results are useful for covariance function estimation based on nonparametric kernel method.

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