Results 21 to 30 of about 104,768 (268)
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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Self‐normalization inference for linear trends in cointegrating regressions
Journal of Time Series Analysis, 2022 In this article, statistical tests concerning the trend coefficient in cointegrating regressions are addressed for the case when the stochastic regressors have deterministic linear trends. The self‐normalization (SN) approach is adopted for developing inferential methods in the integrated and modified ordinary least squares (IMOLS) estimation framework.
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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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Near-optimal inference in adaptive linear regression.
CoRR, 2021 When data is collected in an adaptive manner, even simple methods like ordinary least squares can exhibit non-normal asymptotic behavior. As an undesirable consequence, hypothesis tests and confidence intervals based on asymptotic normality can lead to erroneous results. We propose a family of online debiasing estimators to correct these distributional
Koulik Khamaru +3 more
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IEEE Access, 2021 As the state-of-the-art technology of Bayesian inference, based on low-dimensional principal components analysis (PCA) subspace inference methods can provide approximately accurate predictive distribution and well calibrated uncertainty.
Yongguang Wang, Shuzhen Yao, Tian Xu
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Homoscedasticity: an overlooked critical assumption for linear regression
General Psychiatry, 2019 Linear regression is widely used in biomedical and psychosocial research. A critical assumption that is often overlooked is homoscedasticity. Unlike normality, the other assumption on data distribution, homoscedasticity is often taken for granted when ...
Kun Yang, Justin Tu
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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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Quantile Regression with Generated Regressors
Econometrics, 2021 This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of
Liqiong Chen +2 more
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Revstat Statistical Journal, 2015 This paper develops statistical inference in linear models, dealing with the theory of maximum likelihood estimates and likelihood ratio tests under some linear inequality restrictions on the regression coefficients. The results are widely applicable to
Miguel Fonseca +3 more
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