Results 11 to 20 of about 11,673,454 (251)
Transformation kernel density estimation of actuarial loss functions [PDF]
, 2013 [cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a
Guillén, Montserrat +2 more
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An orthogonal forward regression technique for sparse kernel density estimation [PDF]
, 2008 Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates ...
Chen, Sheng, Hong, X., Harris, Chris J.
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New bandwidth selection for kernel quantile estimators [PDF]
, 2012 Copyright © 2012 Ali Al-Kenani and Keming Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly ...
Al-Kenani, A +3 more
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Maximum kernel likelihood estimation [PDF]
, 2008 We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
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Mathematics, 2022 For a fixed time, t, and a horizon time, b, the probability of default (PD) measures the probability that an obligor, that has paid his/her credit until time t, runs into arrears not later that time t+b.
Rebeca Peláez +2 more
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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Functional Ergodic Time Series Analysis Using Expectile Regression
Mathematics, 2022 In this article, we study the problem of the recursive estimator of the expectile regression of a scalar variable Y given a random variable X that belongs in functional space.
Fatimah Alshahrani +5 more
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kernlab - An S4 package for kernel methods in R [PDF]
, 2004 kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 object model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels),
Zeileis, Achim +10 more
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Attributed Relational SIFT-Based Regions Graph: Concepts and Applications
Machine Learning and Knowledge Extraction, 2020 In the real world, structured data are increasingly represented by graphs. In general, the applications concern the most varied fields, and the data need to be represented in terms of local and spatial connections.
Mario Manzo
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