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About 10,000 papers for “density estimation”

Density Estimation

David W. Scott · Wiley StatsRef: Statistics Reference Online · 2014

Abstract The “density estimation” article highlights the dual roles of the graphical exploration of data as well as the nonparametric estimation of probabilities. Estimators described in detail include the histogram, frequency polygon, averaged shifted histogram, and kernel method. The basic theory of estimation is outlined. Data‐based calibration by cross‐validation is described. Several concrete examples are given.

Cited by 1

Density estimation

Simon J. Sheather · 2004

Abstract. This paper provides a practical description of density estimation based on kernel methods. An important aim is to encourage practicing statisticians to apply these methods to data. As such, reference is made to implementations of these methods in R, S-PLUS and SAS. Key words and phrases: Kernel density estimation, bandwidth selection, local likelihood density estimates, data sharpening. 1.

Cited by 0

Density Estimation

Brani Vidaković · Wiley series in probability and statistics · 1999

The prelims comprise: Orthogonal Series Density Estimators Wavelet Density Estimation Non-Linear Wavelet Density Estimators Non-Negative Density Estimators Other Methods Exercises

Cited by 1

Density Estimation

Paul H. Kvam, Brani Vidaković · Wiley series in probability and statistics · 2007

This chapter contains sections titled: Histogram Kernel and Bandwidth Exercises References

Cited by 0

Density Estimation

Paul H. Kvam, Brani Vidaković, Seong-joon Kim · Wiley series in probability and statistics · 2022

Probability density estimation goes hand in hand with nonparametric estimation of the cumulative distribution function. The histogram provides a quick picture of the underlying density by weighting fixed intervals according the their relative frequency in the data. The idea of the density estimator is to spread out the weight of a single observation in a plot of the empirical density function. The chapter focuses on the kernel density estimator that more fairly spreads out the probability mass of each observation, not arbitrarily in a fixed interval, but smoothly around the observation, typically in a symmetric way. The smoothing function is a positive sequence of bandwidths analogous to the bin width in a histogram. The default estimate is based on a Gaussian kernel; to use another kernel, just enter “rectangular,” “triangular,” or “epanechnikov”.

Cited by 1

Density Estimation

David W. Scott · Encyclopedia of Biostatistics · 2005

Abstract The “density estimation” article highlights the dual roles of the graphical exploration of data as well as the nonparametric estimation of probabilities. Estimators described in detail include the histogram, frequency polygon, averaged shifted histogram, and kernel method. The basic theory of estimation is outlined. Data‐based calibration by cross‐validation is described. Several concrete examples are given.

Cited by 1

Density Estimation

Simon J. Sheather · Statistical Science · 2004

This paper provides a practical description of density estimation based on kernel methods. An important aim is to encourage practicing statisticians to apply these methods to data. As such, reference is made to implementations of these methods in R, S-PLUS and SAS.

Cited by 479Open access

Kernel Density Estimation

Ivana Horová · Wiley StatsRef: Statistics Reference Online · 2014

Abstract The probability density function is a fundamental concept in statistics. Density estimation is the reconstruction of the density function from a set of observed data and it can give valuable indication of such features as skewness and multimodality in the data.

Cited by 6

Evidence Density Estimation

Peter Demján · OSF Preprints (OSF Preprints) · 2019

The Evidence Density Estimation (EDE) function can be applied on radiocarbon-dated as well as typo-chronologically dated archaeological evidence to produce spatio-temporal distribution maps or summed distributions representing intensity of settlement activities within the examined area.

Cited by 0Open access

Boosting Density Estimation

Saharon Rosset Department, Saharon Rosset, Eran Segal · 2002

Several authors have suggested viewing boosting as a gradient descent search for a good fit in function space. We apply gradient-based boosting methodology to the unsupervised learning problem of density estimation. We show convergence properties of the algorithm and prove that a strength of weak learnability property applies to this problem as well. We illustrate the potential of this approach through experiments with boosting Bayesian networks to learn density models.

Cited by 0