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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Variational Inference in high-dimensional linear regression
, 2021 39 ...
Mukherjee, Sumit, Sen, Subhabrata
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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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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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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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ModularBoost: an efficient network inference algorithm based on module decomposition
BMC Bioinformatics, 2021 Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that ...
Xinyu Li +3 more
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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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Assessing NARCCAP climate model effects using spatial confidence regions [PDF]
Advances in Statistical Climatology, Meteorology and Oceanography, 2017 We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models.
J. P. French +2 more
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