Results 31 to 40 of about 113,075 (317)
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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Penalized additive regression for space-time data: a Bayesian perspective [PDF]
, 2003 We propose extensions of penalized spline generalized additive models for analysing space-time regression data and study them from a Bayesian perspective.
Stefan Lang +5 more
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Restricted Inference in Circular-Linear and Linear-Circular Regression
Sri Lankan Journal of Applied Statistics, 2016 In this paper, we investigate restricted inference on two types of circular regression, called circular-linear and linear-circular. Our aim in this paper is to propose an alternative method which is necessary to apply where one observes a weak association between circular dependent and linear predictor variables, or between linear dependent and ...
Thelge Buddika Peiris, Sungsu Kim
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Physics Letters B, 2022 We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers.
Asad Khan, E.A. Huerta, Prayush Kumar
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Heteroscedasticity-Robust Inference in Linear Regression Models With Many Covariates [PDF]
Journal of the American Statistical Association, 2020 We consider inference in linear regression models that is robust to heteroskedasticity and the presence of many control variables. When the number of control variables increases at the same rate as the sample size the usual heteroskedasticity-robust estimators of the covariance matrix are inconsistent.
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A study of partial F tests for multiple linear regression models [PDF]
, 2007 Partial F tests play a central role in model selections in multiple linear regression models. This paper studies the partial F tests from the view point of simultaneous confidence bands.
Jamshidian, M. +4 more
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FEBS Letters, EarlyView.The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source
Bayesian Geoadditive Seemingly Unrelated Regression [PDF]
, 2002 Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A
Steiner, Winfried J. +3 more
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