Results 21 to 30 of about 460,060 (291)
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 Linear Regression Models with Many Covariates and Heteroscedasticity [PDF]
Journal of the American Statistical Association, 2018 The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity.
Cattaneo, Matias D +2 more
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Cluster-Robust Bootstrap Inference in Quantile Regression Models [PDF]
, 2015 In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile regression models. I show that the bootstrap leads to asymptotically valid inference on the entire quantile regression process in a setting with a large ...
Hagemann, Andreas
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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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Nonparametric and semiparametric estimation with discrete regressors [PDF]
, 1994 This paper presents and discusses procedures for estimating regression curves when regressors are discrete and applies them to semiparametric inference problems.
Delgado, Miguel A., Mora, Juan
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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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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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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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