Results 11 to 20 of about 1,128,323 (301)
Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data
Journal of Statistics Education, 2020 Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process.
Karsten Lübke +3 more
doaj +2 more sources
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS [PDF]
Econometric Theory, 2017 This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup.
Zhang, Xinyu, Liu, Chu-An
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How measurement error affects inference in linear regression [PDF]
Empirical Economics, 2020 AbstractMeasurement error biases OLS results. When the measurement error variance in absolute or relative (reliability) form is known, adjustment is simple. We link the (known) estimators for these cases to GMM theory and provide simple derivations of their standard errors. Our focus is on the test statistics.
Erik Meijer +2 more
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Near-optimal inference in adaptive linear regression
Annals of Statistics, 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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Linear Regression and Its Inference on Noisy Network-Linked Data [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2022 AbstractLinear regression on network-linked observations has been an essential tool in modelling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors.
Le, Can M., Li, Tianxi
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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.
Koen Jochmans
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Simultaneous Inference in General Parametric Models [PDF]
Biometrical journal. Biometrische Zeitschrift, 2008 Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates +29 more
core +4 more sources
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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Post-model-selection inference in linear regression models: An integrated review
Statistics Survey, 2022 The research on statistical inference after data-driven model selection can be traced as far back as Koopmans (1949). The intensive research on modern model selection methods for high-dimensional data over the past three decades revived the interest in ...
Dongliang Zhang +2 more
semanticscholar +1 more source
kinkyreg: Instrument-free inference for linear regression models with endogenous regressors
The Stata Journal, 2021 In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables.
Sebastian Kripfganz, J. Kiviet
semanticscholar +1 more source