Results 11 to 20 of about 5,191,119 (260)
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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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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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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On the implementation of LIR: the case of simple linear regression with interval data [PDF]
, 2012 This paper considers the problem of simple linear regression with interval-censored data. That is, n pairs of intervals are observed instead of the n pairs of precise values for the two variables (dependent and independent).
Cattaneo, Marco E.G.V. +2 more
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Frontiers in Psychology, 2022 Within the framework of constrained statistical inference, we can test informative hypotheses, in which, for example, regression coefficients are constrained to have a certain direction or be in a specific order. A large amount of frequentist informative
Caroline Keck, Axel Mayer, Yves Rosseel
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Bayesian Linear Regression [PDF]
, 2009 The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior).
Walter, Gero, Augustin, Thomas
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BMC Medical Research Methodology, 2022 Background Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model ...
Michael Kammer +3 more
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