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Boundary performance of the beta kernel estimators
Journal of Nonparametric Statistics, 2010The beta kernel estimators are shown in Chen [S.X. Chen, Beta kernel estimators for density functions, Comput. Statist. Data Anal. 31 (1999), pp. 131–145] to be non-negative and have less severe boundary problems than the conventional kernel estimator. Numerical results in Chen [S.X. Chen, Beta kernel estimators for density functions, Comput.
Shunpu Zhang, Rohana J. Karunamuni
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Bivariate discrete beta Kernel graduation of mortality data
Lifetime Data Analysis, 2014Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate)
MAZZA, Angelo, PUNZO, ANTONIO
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Consistency of the beta kernel density function estimator
Canadian Journal of Statistics, 2003AbstractThe authors give the exact asymptotic behaviour of the expected average absolute error of a beta kernel density estimator proposed by Chen (1999). They also prove the uniform weak consistency of this estimator for the class of continuous densities.
Bouezmarni, Taoufik, Rolin, Jean-Marie
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Beta kernel estimators for density functions
Computational Statistics & Data Analysis, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Song Xi Chen
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Beta kernel quantile estimators of heavy-tailed loss distributions
Statistics and Computing, 2009In this paper we suggest several nonparametric quantile estimators based on Beta kernel. They are applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte Carlo based study has shown that those estimators improve the efficiency of the traditional ones, not only for light tailed distributions, but ...
Charpentier, Arthur, Oulidi, Abder
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Adaptive Kernel Density Estimation using Beta Kernel
2007 International Conference on Machine Learning and Cybernetics, 2007Adaptive kernel estimation for unit interval compact bounded densities using beta kernel is considered. Beta kernel is an asymmetric kernel that has several particular properties such as variable kernel shapes and matching the unit interval support of the densities to be estimated.
Xun-Fu Yin, Zhi-Feng Hao
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Beta Process Multiple Kernel Learning
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014In kernel based learning, the kernel trick transforms the original representation of a feature instance into a vector of similarities with the training feature instances, known as kernel representation. However, feature instances are sometimes ambiguous and the kernel representation calculated based on them do not possess any discriminative information,
Bingbing Ni, Teng Li, Pierre Moulin
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EM algorithms for beta kernel distributions
Journal of Statistical Computation and Simulation, 2012The EM algorithm is employed to compute maximum-likelihood estimates for beta kernel distributions. Estimation is considered under two censoring schemes: the progressive Type-I censoring and progressive Type-II right censoring schemes. As an application, the EM algorithm is executed to obtain maximum-likelihood estimates for the beta Weibull ...
Mahdi Teimouri +2 more
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Bremsstrahlung Kernel of Beta-Particles
2007Energy distribution of the external bremsstrahlung emitted by the beta-particles slowing down in the selected media was calculated using PENELOPE - a code system for Monte Carlo simulation of electron and photon transport. During the calculation, it was assumed that the beta particles generated at the center of the spherical homogeneous media were ...
Chul-Young Yi +3 more
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