Results 261 to 270 of about 23,658 (304)

Density Distillation for Fast Nonparametric Density Estimation

IEEE Transactions on Neural Networks and Learning Systems, 2023
Nonparametric density estimation has been extensively used in various application scenarios and theoretical models. However, the modeling of these powerful methods is inseparable from the sample data and comes at the cost of repeated and intensive kernel calculations, which makes their efficiency greatly affected by the sample scale, data dimension ...
Bopeng Fang, Shifeng Chen
exaly   +3 more sources

Developments in Nonparametric Density Estimation

International Statistical Review / Revue Internationale de Statistique, 1980
Summary The object of the present study is to summarize recent developments in nonparametric density estimation. The study covers the period of time from 1956 to 1978. Most of the important types of nonparametric density estimations are discussed. These include Parzen or kernel estimators, series estimators, penalized maximum likelihood estimators, and
Bean, Steven J., Tsokos, Chris P.
openaire   +3 more sources

On nonparametric kernel density estimates

Biometrika, 1990
SUMMARY The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error.
M. Samiuddin, G. M. El-Sayyad
openaire   +1 more source

Nonparametric estimates of probability densities

IEEE Transactions on Information Theory, 1975
A disadvantage of the kernel estimate of a probability density is that the degree of smoothing about the observation points is chosen without regard to the data. A modification of the kernel estimate is proposed that allows the data to play a role in this smoothing and, at the same time, retains the desirable features of the kernel estimate.
openaire   +2 more sources

Nonparametric Density Estimation

2004
Contrary 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, Axel Werwatz, Marlene Müller, Stefan Sperlich   +3 more
openaire   +1 more source

On stochastic complexity and nonparametric density estimation

Biometrika, 1988
Let an unknown density f be supported within the rectangular prism \(R\subset R^ k\). R is partitioned into m congruent cells \(C_ i\), for \(1\leq i\leq m\). A histogram estimator of f is defined by the authors to take constant values on the cells, and so their prior is distributed over the class of all such densities, i.e. \(f(x)=mp_ i\), when \(x\in
Hall, Peter, Hannan, E. J.
openaire   +2 more sources

NONPARAMETRIC DENSITY ESTIMATION

2017
This 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 ...
openaire   +2 more sources

Nonparametric estimation of quantile density function

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pooja Soni, Isha Dewan, Kanchan Jain
openaire   +1 more source

Clustering via nonparametric density estimation

Statistics and Computing, 2007
Although 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
openaire   +3 more sources

Multiresolution nonparametric intensity and density estimation

IEEE International Conference on Acoustics Speech and Signal Processing, 2002
This 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
openaire   +1 more source