Results 31 to 40 of about 95,782 (269)
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
openaire +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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.
openaire +1 more source
Statistical Inference for Functional Linear Quantile Regression
, 2022 We propose inferential tools for functional linear quantile regression where the conditional quantile of a scalar response is assumed to be a linear functional of a functional covariate. In contrast to conventional approaches, we employ kernel convolution to smooth the original loss function.
Sang, Peijun, Shang, Zuofeng, Du, Pang
openaire +2 more sources
Calibrated Percentile Double Bootstrap For Robust Linear Regression Inference [PDF]
Statistica Sinica, 2018 We consider inference for the parameters of a linear model when the covariates are random and the relationship between response and covariates is possibly non-linear. Conventional inference methods such as z-intervals perform poorly in these cases.
McCarthy, Daniel +6 more
openaire +2 more sources
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
doaj +1 more source