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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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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Statistical Inference for some Robust Regression Estimators [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Tiǧāriyyaẗ, 2016 The method of least squares of the most commonly used methods for estimating the parameters of linear regression models,this method requires the availability of several assumptions for the capabilities more efficiently.
مشيرة عادل سليمان الأعصر
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A robust gene regulatory network inference method base on Kalman filter and linear regression. [PDF]
PLoS ONE, 2018 The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology.
Jamshid Pirgazi, Ali Reza Khanteymoori
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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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Causality inference of linearly correlated variables: The statistical simulation and regression method [PDF]
Computational Ecology and Software, 2021 Causality inference of variables is a research focus in science. Due to its importance, a statistical simulation and regression method for causality inference of linearly correlated (scale or interval) variables was proposed in present study.
WenJun Zhang
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Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data
Journal of Statistics Education, 2020 Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process.
Karsten Lübke +3 more
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