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Inference in functional linear quantile regression [PDF]
Journal of Multivariate Analysis, 2022 In this paper, we study statistical inference in functional quantile regression for scalar response and a functional covariate. Specifically, we consider a functional linear quantile regression model where the effect of the covariate on the quantile of the response is modeled through the inner product between the functional covariate and an unknown ...
Meng Li +3 more
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Robust Bayesian Regression with Synthetic Posterior Distributions
Entropy, 2020 Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily ...
Shintaro Hashimoto, Shonosuke Sugasawa
doaj +3 more sources
Variational Bayesian inference for linear and logistic regression
, 2019 The article describe the model, derivation, and implementation of variational Bayesian inference for linear and logistic regression, both with and without automatic relevance determination.
Drugowitsch, Jan
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Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model [PDF]
المجلة العراقية للعلوم الاحصائية, 2021 This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model ...
Sarmad Abdulkhaleq Salih, Emad Aboudi
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Adversarial orthogonal regression: Two non-linear regressions for causal inference [PDF]
Neural Networks, 2021 We propose two nonlinear regression methods, named Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) for the general case of structural equation models. Both methods try to make the residual of regression independent from regressors while putting no assumption on noise ...
M. Reza Heydari +2 more
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Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
Ingeniería y Ciencia, 2021 We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions.
Álvaro Alexander Burbano Moreno +2 more
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Evaporation Estimation Using Adaptive Neuro-Fuzzy Inference System and Linear Regression [PDF]
Engineering and Technology Journal, 2014 Evaporation is important for water planning, management and hydrological practices, and it plays an influential role in the management and development of water resources.
Ali H. Al-Aboodi
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Life, 2022 The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the ...
Zachariah Madaki +5 more
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Inference of gene regulatory networks from genetic perturbations with linear regression model. [PDF]
PLoS ONE, 2013 It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes.
Zijian Dong, Tiecheng Song, Chuang Yuan
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Bayesian estimation of directed functional coupling from brain recordings. [PDF]
PLoS ONE, 2017 In many fields of science, there is the need of assessing the causal influences among time series. Especially in neuroscience, understanding the causal interactions between brain regions is of primary importance.
Danilo Benozzo +4 more
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