Results 81 to 90 of about 3,774,080 (285)

Harnessing machine learning and optimization for informed chemical engineering decisions: A styrene reactor analysis

The Canadian Journal of Chemical Engineering, EarlyView.
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin, Clémence Fauteux‐Lefebvre, Jules Thibault   +2 more
wiley   +1 more source

A Bayesian approach to parameter estimation for kernel density estimation via transformations [PDF]

In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations.
David Pitt, Xueyuan Wu, Qing Liu, Xibin Zhang   +3 more
core  

Bayesian inverse ensemble forecasting for COVID‐19

Canadian Journal of Statistics, EarlyView.
Abstract Variations in strains of COVID‐19 have a significant impact on the rate of surges and on the accuracy of forecasts of the epidemic dynamics. The primary goal for this article is to quantify the effects of varying strains of COVID‐19 on ensemble forecasts of individual “surges.” By modelling the disease dynamics with an SIR model, we solve the ...
Kimberly Kroetch, Don Estep
wiley   +1 more source

Deconvolution Estimation in Measurement Error Models: The R Package decon

Journal of Statistical Software, 2011
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models.
Xiao-Feng Wang, Bin Wang
doaj  

On the effect of fixed-bandwidth kernel density estimation on the exponential distribution

Journal of Nigerian Society of Physical Sciences
Kernel density estimation (KDE) is widely used as a nonparametric smoothing operator in statistics. In this work, we study fixed-bandwidth KDE as a convolution operator applied to an exponential baseline distribution with rate parameter (beta > 0).
Anwar Bataihah
doaj   +1 more source

Asymptotic Normality of Conditional Density and Conditional Mode in the Functional Single Index Model

Ekonometria, 2021
The main objective of this paper is to investigate the nonparametric estimation of the conditional density of a scalar response variable Y, given the explanatory variable X taking value in a Hilbert space when the sample of observations is considered as ...
Fatima Akkal, Nadia Kadiri, Abbes Rabhi
doaj  

Applications of nonparametric kernel-type density estimators

, 1986
Bibliography: p. 125-127.
openaire   +2 more sources

A note on nonparametric density deconvolution by weighted kernel estimators

Journal of the Korean Data and Information Science Society, 2014
Abstract Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimatorfor the deconvolution problem. In the case of Gaussian kernels and measurement er-ror, they argued that the weighted kernel density estimator is a competitive estimatorover the classical deconvolution kernel estimator.
openaire   +2 more sources

Asymptotic Theory for Zero Energy Density Estimation with Nonparametric Regression Applications [PDF]

A local limit theorem is given for the sample mean of a zero energy function of a nonstationary time series involving twin numerical sequences that pass to infinity.
Peter C. B. Phillips, Qiying Wang
core  

Nonlinear permuted Granger causality

Canadian Journal of Statistics, EarlyView.
Abstract Granger causality is an established, contentious method that seeks causal temporal connections via association and precedence. While not true causal inference, it assists in mapping networks of information flow that may warrant further study.
Noah D. Gade, Jordan Rodu
wiley   +1 more source