Results 271 to 280 of about 23,658 (304)

On nonparametric local inference for density estimation

Computational Statistics & Data Analysis, 2010
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Ngai Hang Chan, Thomas C. M. Lee, Liang Peng   +2 more
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A Fast Algorithm for Nonparametric Probability Density Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
A fast algorithm for the well-known Parzen window method to estimate density functions from the samples is described. The computational efforts required by the conventional and straightforward implementation of this estimation procedure limit its practical application to data of low dimensionality.
Jack-Gérard Postaire, Christian Vasseur
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Nonparametric Density Estimation

1996
In the linear model y = x′β + u where x is a regressor vector and E(u | x)= 0, we estimate β in E(y | x)= x′β However, the assumption of the linear model, or any nonlinear model for that matter, is a strong one. In nonparametric regression, we try to estimate E(y | x) without specifying the functional form. Since $$E(y{\kern 1pt} |{\kern 1pt} x) = \
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Efficient On-Line Nonparametric Kernel Density Estimation

Algorithmica, 1999
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Christophe G. Lambert, S. E. Harrington, C. R. Harvey, A. Glodjo   +3 more
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Nonparametric Density Estimation

2013
Nonparametric techniques consist of sophisticated alternatives to traditional parametric models for studying multivariate data. What makes these alternative techniques so appealing to the data analyst is that they make no specific distributional assumptions and, thus, can be employed as an initial exploratory look at the data.
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Nonparametric Density Estimation by B-spline Duality

SSRN Electronic Journal, 2019
In this article, we propose a new nonparametric density estimator derived from the theory of frames and Riesz bases. In particular, we propose the so-called bi-orthogonal density estimator based on the class of B-splines and derive its theoretical properties, including the asymptotically optimal choice of bandwidth.
Cui, Zhenyu, Kirkby, Justin Lars, Nguyen, Duy   +2 more
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On nonparametric estimation of a functional of a probability density

IEEE Transactions on Information Theory, 1986
Let \(\{h_ k\}\) be the Hermite orthonormal system over the real line, and let \(X_ 1,...,X_ n\) be i.i.d. with density f. Parseval's identity suggests the following estimate \(\hat I\) of \(I=\int f^ 2(x)dx:\) \[ \hat I=\sum^{N(n)}_{k=0}[\frac{1}{n(n-1)}\sum_{i\neq j}h_ k(X_ i)h_ k(X_ j)].
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Nonparametric Estimation of a Density of Unknown Smoothness

Theory of Probability & Its Applications, 1986
Translation from Teor. Veroyatn. Primen. 30, No.3, 524-534 (Russian) (1985; Zbl 0581.62035).
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Oversmoothed Nonparametric Density Estimates

Journal of the American Statistical Association, 1985
Abstract The optimal histogram for a sample of size n from a density defined on the entire real line requires at least (2n)1/3 bins, under mild smoothness conditions. Similar bounds exist for the frequency polygon and kernel estimators. Values near these bounds give nearly optimal results for a variety of smooth densities, providing good, quick density
George R. Terrell, David W. Scott
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Nonparametric Density Estimation - A Numerical Exploration

2022 Winter Simulation Conference (WSC), 2022
Paul F. Evangelista, Vikram Mittal
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