Results 21 to 30 of about 113,075 (317)
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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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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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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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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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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