Results 21 to 30 of about 52,951 (307)

Conditional density estimation with class probability estimators [PDF]

, 2009
Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it.
Remco R. Bouckaert, Eibe Frank, Frank, Eibe, Bouckaert, Remco R.   +3 more
core   +1 more source

New Multivariate Product Density Estimators

Journal of Multivariate Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Devroye, Luc, Krzyżak, Adam
openaire   +1 more source

Multivariate Tail Probabilities: Predicting Regional Pertussis Cases in Washington State

Entropy, 2021
In disease modeling, a key statistical problem is the estimation of lower and upper tail probabilities of health events from given data sets of small size and limited range.
Xuze Zhang, Saumyadipta Pyne, Benjamin Kedem   +2 more
doaj   +1 more source

The DIA-Method for Navigational Integrity [PDF]

TransNav, 2023
In this contribution we present a review of the DIA-method to ensure navigational integrity. The DIA-method rigorously combines parameter estimation and statistical testing for the Detection, Identification and Adaptation of multivariate and multiple ...
Peter J.G. Teunissen
doaj   +1 more source

Multivariate Normal Variance Mixtures in R: The R Package nvmix

Journal of Statistical Software, 2022
We present the features and implementation of the R package nvmix for the class of normal variance mixtures including Student t and normal distributions.
Erik Hintz, Marius Hofert, Christiane Lemieux   +2 more
doaj   +1 more source

A Nonparametric Estimate of a Multivariate Density Function

The Annals of Mathematical Statistics, 1965
Let $x_1, \cdots, x_n$ be independent observations on a $p$-dimensional random variable $X = (X_1, \cdots, X_p)$ with absolutely continuous distribution function $F(x_1, \cdots, x_p)$. An observation $x_i$ on $X$ is $x_i = (x_{1i}, \cdots, x_{pi})$. The problem considered here is the estimation of the probability density function $f(x_1, \cdots, x_p ...
Loftsgaarden, D. O., Quesenberry, C. P.
openaire   +3 more sources

Multivariate mixed kernel density estimators and their application in machine learning for classification of biological objects based on spectral measurements [PDF]

Компьютерная оптика, 2019
A problem of non-parametric multivariate density estimation for machine learning and data augmentation is considered. A new mixed density estimation method based on calculating the convolution of independently obtained kernel density estimates for ...
Alexander Sirota, Artem Donskikh, Alexey Akimov, Dmitry Minakov   +3 more
doaj   +1 more source

Multivariate locally adaptive density estimation [PDF]

Computational Statistics & Data Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Probabilistic Prediction of Oceanographic Velocities with Multivariate Gaussian Natural Gradient Boosting

Environmental Data Science, 2023
Many single-output regression problems require estimates of uncertainty along with the point predictions. For this purpose, there exists a class of regression algorithms that predict a conditional distribution rather than a point estimate.
Michael O’Malley, Adam M. Sykulski, Rick Lumpkin, Alejandro Schuler   +3 more
doaj   +1 more source

Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation

Mathematics, 2020
A soft-constrained neural network for density estimation (SC-NN-4pdf) has recently been introduced to tackle the issues arising from the application of neural networks to density estimation problems (in particular, the satisfaction of the second ...
Edmondo Trentin
doaj   +1 more source