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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ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE [PDF]
Econometric Theory, 2016 We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function.
Kato, Ryutah, Sasaki, Yuya
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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
doaj
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS [PDF]
Econometric Theory, 2017 This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup.
Zhang, Xinyu, Liu, Chu-An
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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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Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river [PDF]
Global Journal of Environmental Science and Management, 2018 : In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve ...
G. Elkiran +3 more
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Permutation inference for the general linear model [PDF]
, 2014 Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data.
Winkler, Anderson +13 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
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Comparison of artificial intelligence methods for predicting compressive strength of concrete
Građevinar, 2021 Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time.
Mehmet Timur Cihan
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Inference in High-dimensional Linear Regression
, 2021 31 pages, 4 figures, 7 ...
Battey, Heather S., Reid, Nancy
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