Results 61 to 70 of about 257,461 (188)

Alternating kernel and mixture density estimates

Computational Statistics & Data Analysis, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Priebe, C. E., Marchette, D. J.
openaire   +2 more sources

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
doaj  

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  

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

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  

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
doaj   +1 more source

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 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

Robust Kernel Density Function Estimation [PDF]

, 2010
The classical kernel density estimation technique is the commonly used method to estimate the density function. It is now evident that the accuracy of such density function estimation technique is easily affected by outliers.
Dadkhah, Kourosh
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