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Nonparametric estimation of quantile density function
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pooja Soni, Isha Dewan, Kanchan Jain
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NONPARAMETRIC DENSITY ESTIMATION
2017This chapter presents a background material, describing the fundamental concepts related to the nonparametric density estimation. First, a well-known histogram technique is briefly presented together with a description of its main drawbacks. To avoid the highlighted problems, at least to some extent, one might use a smart histogram modification known ...
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Nonparametric Density Estimation
2004Contrary to the treatment of the histogram in statistics textbooks we have shown that the histogram is more than just a convenient tool for giving a graphical representation of an empirical frequency distribution. It is a serious and widely used method for estimating an unknown pdf.
Wolfgang Härdle +3 more
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On nonparametric local inference for density estimation
Computational Statistics & Data Analysis, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ngai Hang Chan +2 more
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Multiresolution nonparametric intensity and density estimation
IEEE International Conference on Acoustics Speech and Signal Processing, 2002This paper introduces a new multiscale method for nonparametric piecewise polynomial intensity and density estimation of point processes. Fast, piecewise polynomial, maximum penalized likelihood methods for intensity and density estimation are developed.
Rebecca M. Willett, Robert D. Nowak
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Clustering via nonparametric density estimation
Statistics and Computing, 2007Although Hartigan (1975) had already put forward the idea of connecting identification of subpopulations with regions with high density of the underlying probability distribution, the actual development of methods for cluster analysis has largely shifted towards other directions, for computational convenience.
AZZALINI A, TORELLI, Nicola
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Nonparametric Density Estimation
1996In 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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Nonparametric Density Estimation
2013Nonparametric 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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A Fast Algorithm for Nonparametric Probability Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982A 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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On nonparametric estimation of a functional of a probability density
IEEE Transactions on Information Theory, 1986Let \(\{h_ k\}\) 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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