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Manifold learning based on kernel density estimation

Учёные записки Казанского университета: Серия Физико-математические науки, 2018
The problem of unknown high-dimensional density estimation has been considered. It has been suggested that the support of its measure is a low-dimensional data manifold. This problem arises in many data mining tasks.
A.P. Kuleshov, A.V. Bernstein, Yu.A. Yanovich   +2 more
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The Estimation of Conditional Densities [PDF]

We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates.
Oliver Linton, Peter M Robinson, Xiaohong Chen   +2 more
core  

Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation

, 2019
Kernel density estimation is a well known method involving a smoothing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used the bandwidth selection remains a challenging issue in terms of balancing ...
Lacour, Claire, Massart, Pascal, Rivoirard, Vincent, Varet, Suzanne   +3 more
core  

Kernel Density Estimators for Gaussian Mixture Models

Lithuanian Journal of Statistics, 2013
The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
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Kernel deconvolution density estimation

, 2018
This dissertation is about kernel deconvolution density estimation (KDDE), which is nonparametric density estimation based on a sample contaminated with measurement error. It is separated in four parts. First we explore some methodological aspects of KDDE.
openaire   +3 more sources

A general framework for statistical inference on discrete event systems. [PDF]

We present a framework for statistical analysis of discrete event systems which combines tools such as simulation of marked point processes, likelihood methods, kernel density estimation and stochastic approximation to enable statistical analysis of the ...
Koning, A.J., Nicolai, R.P.
core   +1 more source

Kernel density estimation for stationary random fields [PDF]

, 2014
In this paper, under natural and easily verifiable conditions, we prove the $\mathbb{L}^1$-convergence and the asymptotic normality of the Parzen-Rosenblatt density estimator for stationary random fields of the form $X_k = g\left(\varepsilon_{k-s}, s \in
Machkouri, Mohamed El
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Nonparametric Density Estimation for Linear Processes with Infinite Variance [PDF]

We consider nonparametric estimation of marginal density functions of linear processes by using kernel density estimators. We assume that the innovation processes are i.i.d. and have infinite-variance.
Honda, Toshio
core  

Traffic Accident Spatial Modeling Using Adaptive Kernel Density Estimation Method Based on Geographical Information Systems For Road Sections In Brebes District, Brebes Regency

Journal of Applied Geospatial Information
Based on information from the Satlantas Polres Bebes, in 2022 the number of accidents in the Brebes Regency area reached 1.088 incidents that cause fatality damage and material losses. Data shows that in the last three years, Brebes District has recorded
Tyas Fitria Andini, Moehammad Awaluddin, Arief Laila Nugraha   +2 more
doaj   +1 more source

Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data [PDF]

Journal of Sciences, Islamic Republic of Iran, 2014
Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the ...
V. Fakoor
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