Results 31 to 40 of about 460,060 (291)
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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Inference about the slope in linear regression: an empirical likelihood approach [PDF]
, 2017 We present a new, efficient maximum empirical likelihood estimator for the slope in linear regression with independent errors and covariates. The estimator does not require estimation of the influence function, in contrast to other approaches, and is ...
Müller, Ursula U. +2 more
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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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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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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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Cluster-Robust Variance Estimation for Dyadic Data [PDF]
, 2015 Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that ...
Aronow, Peter M. +2 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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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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Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It [PDF]
, 2017 We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting.
Grünwald, Peter, van Ommen, Thijs
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Bayesian Inference in Numerical Cognition: A Tutorial Using JASP
Journal of Numerical Cognition, 2020 Researchers in numerical cognition rely on hypothesis testing and parameter estimation to evaluate the evidential value of data. Though there has been increased interest in Bayesian statistics as an alternative to the classical, frequentist approach to ...
Thomas J. Faulkenberry +2 more
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