Results 21 to 30 of about 195,855 (264)

Performance Comparison of Parametric and Non-Parametric Regression Models for Uncertainty Analysis of Sheet Metal Forming Processes

open access: yesMetals, 2020
This work aims to compare the performance of various parametric and non-parametric metamodeling techniques when applied to sheet metal forming processes. For this, the U-Channel and the Square Cup forming processes were studied.
Armando E. Marques   +5 more
doaj   +1 more source

Parametrically Guided Non‐parametric Regression [PDF]

open access: yesScandinavian Journal of Statistics, 1998
We present a new approach to regression function estimation in which a non‐parametric regression estimator is guided by a parametric pilot estimate with the aim of reducing the bias. New classes of parametrically guided kernel weighted local polynomial estimators are introduced and formulae for asymptotic expectation and variance, hence approximated ...
openaire   +2 more sources

Non-parametric regression for robot learning on manifolds

open access: yesCoRR, 2023
17 pages, 15 figures; added quantitative comparisons with baselines in the experiments Section; modified introduction; fixed typos; added Appendixes B and C; reordered sections for better understanding; changed the Section on adaptation of ...
Pablo C. López-Custodio   +3 more
openaire   +2 more sources

Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2

open access: yesRemote Sensing, 2020
During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the
Anna Derkacheva   +4 more
doaj   +1 more source

Estimación no paramétrica de la densidad y de la regresión - previsión no paramétrica

open access: yesRevista de Matemática: Teoría y Aplicaciones, 2009
We begin with a short wiew of non parametric estimation of density and regression; then we give details and interpretation of a forecasting method, called non parametric forecasting .
Michel Carbon, Christian Francq
doaj   +1 more source

Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness

open access: yesEnergies, 2021
Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities.
Takuji Matsumoto, Yuji Yamada
doaj   +1 more source

Gender gap in agricultural productivity in Nigeria: A commodity level analysis [PDF]

open access: yesEkonomika Poljoprivrede (1979), 2017
This study assesses gender gap in agricultural productivity across selected major crops grown by Nigerian farmers including cassava, yam, maize, guinea corn, bean and millet.
Olakojo Abayomi Solomon
doaj   +1 more source

Structural and weather-related factors of the sustainable intensification process in agriculture of the European Union regions

open access: yesAgricultural Economics (AGRICECON), 2023
Sustainable intensification (SI) is a widely discussed concept that aims to increase agricultural production without harming the environment. This study assessed the process of SI that took place in the EU regions from 2004 to 2018 and the impact of ...
Jakub Staniszewski, Anika Muder
doaj   +1 more source

Adaptive Robust Efficient Methods for Periodic Signal Processing Observed with Colours Noises

open access: yesAdvances in Electrical and Electronic Engineering, 2019
In this paper, we consider the problem of robust adaptive efficient estimating a periodic signal observed in the transmission channel with the dependent noise defined by non-Gaussian Ornstein-Uhlenbeck processes with unknown correlation properties ...
Evgeny Pchelintsev   +2 more
doaj   +1 more source

On the pitfalls of Gaussian likelihood scoring for causal discovery

open access: yesJournal of Causal Inference, 2023
We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
doaj   +1 more source

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