Results 41 to 50 of about 95,782 (269)
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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Near-optimal inference in adaptive linear regression
, 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
Khamaru, Koulik +4 more
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Inference in Linear Regression Models with Many Covariates and Heteroscedasticity [PDF]
Journal of the American Statistical Association, 2017 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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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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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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Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Cognitive and psychological difficulties could negatively interfere with treatment adherence and quality of life before and after hematopoietic stem cell transplant (HSCT). Methods to mitigate these changes may have positive effects on treatment success.
Kristen L. Votruba +11 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background L‐asparaginase is a critical component in treatment protocols for pediatric acute lymphoblastic leukemia. Acute pancreatitis reactions can necessitate delays and, in some cases, discontinuation of L‐asparaginase, which compromises outcomes.
Edward J. Raack +39 more
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Some remarks on a pair of seemingly unrelated regression models
Open Mathematics, 2019 Linear regression models are foundation of current statistical theory and have been a prominent object of study in statistical data analysis and inference.
Hou Jian, Zhao Yong
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Fitting Additive Binomial Regression Models with the R Package blm
Journal of Statistical Software, 2013 The R package blm provides functions for fitting a family of additive regression models to binary data. The included models are the binomial linear model, in which all covariates have additive effects, and the linear-expit (lexpit) model, which allows ...
Stephanie Kovalchik, Ravi Varadhan
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