Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
Sign Up to like & getrecommendations! Published in 2018 at "Stochastic Environmental Research and Risk Assessment"
DOI: 10.1007/s00477-018-1573-6
Abstract: The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to “quantile crossing”, where regression predictions for different quantile… read more here.
Keywords: quantile regression; regression; model; non crossing ... See more keywords
The large deviation results for the nonlinear regression model with dependent errors
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DOI: 10.1007/s11749-016-0509-z
Abstract: In this paper, we investigate the least squares (LS) estimator of the nonlinear regression model based on the extended negatively dependent errors which are widely dependent structures. Under the general conditions, we establish some large… read more here.
Keywords: regression; deviation results; regression model; large deviation ... See more keywords
Robust inference for nonlinear regression models
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DOI: 10.1007/s11749-017-0570-2
Abstract: A family of weighted estimators of the regression parameter under a nonlinear model is introduced. The proposed weighted estimators are computed through a four-step MM-procedure, and the given approach allows for possible missing responses. Under… read more here.
Keywords: robust inference; nonlinear regression; inference nonlinear; regression models ... See more keywords
Nonlinear regression models with general distortion measurement errors
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DOI: 10.1080/00949655.2019.1586904
Abstract: This paper considers nonlinear regression models when neither the response variable nor the covariates can be directly observed, but are measured with both multiplicative and additive distortion measurement errors. We propose conditional variance and conditional… read more here.
Keywords: distortion measurement; regression models; measurement errors; nonlinear regression ... See more keywords
A novel perspective for parameter estimation of seemingly unrelated nonlinear regression
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DOI: 10.1080/02664763.2021.1877638
Abstract: Nonlinear regression is commonly used as a modeling tool to get a functional form between inputs and response variables when the inputs and the responses have a nonlinear relationship. It should be better to compose… read more here.
Keywords: parameter estimation; parameter; seemingly unrelated; nonlinear regression ... See more keywords
Nonlinear Regression-Based GNSS Multipath Dynamic Map Construction and Its Application in Deep Urban Areas
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2023.3246493
Abstract: GNSS signals are easily blocked or degraded because of the dense presence of high-rise buildings in urban areas, and positioning errors arising from reflected signals amount to as much as hundreds of meters. Various conventional… read more here.
Keywords: urban areas; regression based; multipath; based gnss ... See more keywords
Nonlinear Regression via Deep Negative Correlation Learning
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DOI: 10.1109/tpami.2019.2943860
Abstract: Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning, two common solutions exist i) transforming nonlinear regression to a robust… read more here.
Keywords: regression; nonlinear regression; negative correlation; proposed method ... See more keywords
Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2019.2917712
Abstract: The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which… read more here.
Keywords: maximum correntropy; extended kalman; nonlinear regression; correntropy ... See more keywords
Improving spring–mass parameter estimation in running using nonlinear regression methods
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DOI: 10.1242/jeb.232850
Abstract: ABSTRACT Runners are commonly modeled as spring–mass systems, but the traditional calculations of these models rely on discrete observations during the gait cycle (e.g. maximal vertical force) and simplifying assumptions (e.g. leg length), challenging the… read more here.
Keywords: mass; spring mass; using nonlinear; parameter ... See more keywords
Developing a Model for Curve-Fitting a Tree Stem’s Cross-Sectional Shape and Sapwood–Heartwood Transition in a Polar Diagram System Using Nonlinear Regression
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DOI: 10.3390/f14061102
Abstract: A function from the domain (x-set) to the codomain (y-set) connects each x element to precisely one y element. Since each x-point originating from the domain corresponds to two y-points on the graph of a… read more here.
Keywords: curve; stem cross; tree stem; model ... See more keywords