Results 281 to 290 of about 1,128,323 (301)
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Predictive inference on equicorrelated linear regression models
Applied Mathematics and Computation, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Khan, S, Bhatti, MI
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Jackknife inference for heteroscedastic linear regression models
Canadian Journal of Statistics, 1993AbstractInference on the regression parameters in a heteroscedastic linear regression model with replication is considered, using either the ordinary least‐squares (OLS) or the weighted least‐squares (WLS) estimator. A delete‐group jackknife method is shown to produce consistent variance estimators irrespective of within‐group correlations, unlike the ...
Shao, Jun, Rao, J. N. K.
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Inference After Variable Selection in Linear Regression Models
Biometrika, 1992Summary: We explore the impact of variable selection on statistical inferences in linear regression models. In particular, the generalized final prediction error criterion of \textit{R. Shibata} [ibid. 71, 43-49 (1984; Zbl 0543.62053)] is considered and it is found, among other things, that inferences on the regression coefficients are impaired by the ...
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Inference on varying-coefficient partially linear regression model
Acta Mathematicae Applicatae Sinica, English Series, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Feng, Jing-Yan +2 more
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Statistical inference for multivariate partially linear regression models
Canadian Journal of Statistics, 2013AbstractIn this paper we study a class of multivariate partially linear regression models. Various estimators for the parametric component and the nonparametric component are constructed and their asymptotic normality established. In particular, we propose an estimator of the contemporaneous correlation among the multiple responses and develop a test ...
You, Jinhong, Zhou, Yong, Chen, Gemai
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Bayesian inference for high‐dimensional linear regression under mnet priors
Canadian Journal of Statistics, 2016AbstractFor regression problems that involve many potential predictors, the Bayesian variable selection (BVS) method is a powerful tool. This method associates each model with its posterior probability and achieves excellent prediction performance through Bayesian model averaging.
Tan, Aixin, Huang, Jian
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Post-J test inference in non-nested linear regression models
Science China Mathematics, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, XinJie +3 more
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Permutation inference distribution for linear regression and related models
Journal of Nonparametric Statistics, 2019ABSTRACTFor linear regression and related models, a permutation inference distribution (PID) is introduced.
Qiang Wu, Paul Vos
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Bootstrap inference in functional linear regression models
We consider functional linear regression models (FLRMs) with functional regressor and scalar response, where the inference of the slope function is an important problem. However, even though asymptotic inference methods exist in FLRMs, these methods are limited in applicability because a wrong scaling factor is used; truncation bias in the limit is ...openaire +2 more sources
Exact inference in the inequality constrained normal linear regression model
Journal of Applied Econometrics, 1986AbstractInference in the inequality constrained normal linear regression model is approached as a problem in Bayesian inference, using a prior that is the product of a conventional uninformative distribution and an indicator function representing the inequality constraints.
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