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Multiparameter Kernel Estimates
2001In this chapter we illustrate how the minimum distance estimate described in Chapter 10 may be used to select various parameters simultaneously in an almost optimal manner. The examples are all simple multiparameter versions of the kernel estimate. Once again, the methods applied here are fully combinatorial, as the only thing we need in each case is a
Luc Devroye, Gábor Lugosi
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Kernel Estimates of Dose Response
Biometrics, 1988A nonparametric method for analyzing quantal response data from an indirect bioassay experiment is proposed. Kernel estimates of the dose-response curve are used to develop approximate confidence intervals for (i) the optimal combination dose of a drug with therapeutic effects at low doses and toxic effects at high doses, and (ii) the lethal dose ...
J G, Staniswalis, V, Cooper
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2017
This chapter describes the kernel density estimation technique that can be considered a smoothed version of the Parzen windows presented in the Chapter 2. First, the most popular kernel types are presented together with a number of basic definitions both for uni- and multivariate cases and then a review of performance criteria is provided, starting ...
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This chapter describes the kernel density estimation technique that can be considered a smoothed version of the Parzen windows presented in the Chapter 2. First, the most popular kernel types are presented together with a number of basic definitions both for uni- and multivariate cases and then a review of performance criteria is provided, starting ...
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Kernel estimates for Schrödinger operators
Journal of Evolution Equations, 2006We prove short time estimates for the heat kernel of Schrodinger operators with unbounded potential in R N .
METAFUNE, Giorgio Gustavo Ermanno +2 more
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2012
A boosting algorithm [1, 2] could be seen as a way to improve the fit of statistical models. Typically, M predictions are operated by applying a base procedure—called a weak learner—to M reweighted samples. Specifically, in each reweighted sample an individual weight is assigned to each observation.
DI MARZIO, Marco, Taylor Charles
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A boosting algorithm [1, 2] could be seen as a way to improve the fit of statistical models. Typically, M predictions are operated by applying a base procedure—called a weak learner—to M reweighted samples. Specifically, in each reweighted sample an individual weight is assigned to each observation.
DI MARZIO, Marco, Taylor Charles
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Kernel Nonparametric Estimation
2008Some regression models are fully parametric in that both the regression function and the error term distribution are parametrically specified, whereas some are semiparametric in the sense that only the regression function is parametrically specified—LSE is semiparametric in this sense.
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Multivariate Kernel Estimators
1988The kernel estimate (4.4) can be generalized to the case of a multivariate regression function g: A → ℝ where A ⊂ ℝm, m ≥ 1. The proofs usually can be generalized from the univariate case without difficulty. There are, however, some genuinely new features in the multivariate situation.
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1991
The idea of kernel estimators was introduced by Rosenblatt (1956). In Chapter 1 needles were used in the observations as a very noisy method to approximate density.
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The idea of kernel estimators was introduced by Rosenblatt (1956). In Chapter 1 needles were used in the observations as a very noisy method to approximate density.
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Analysing curves using kernel estimators
Pediatric Nephrology, 1991In this paper a novel statistical method for curve fitting is described and applied to growth data for illustration. This technique, called kernel estimation, is non-parametric and belongs to the class of smoothing methods. Therefore, it does not need an a priori functional model where individual parameters are determined from the data.
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