Bootstrapping for multivariate linear regression models

@article{Eck2017BootstrappingFM,
  title={Bootstrapping for multivariate linear regression models},
  author={Daniel J. Eck},
  journal={arXiv: Statistics Theory},
  year={2017}
}
  • Daniel J. Eck
  • Published 24 April 2017
  • Mathematics
  • arXiv: Statistics Theory
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33 Citations
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References

SHOWING 1-10 OF 12 REFERENCES

Building Multiple Regression Models Interactively

Automated multiple regression model-building techniques often hide important aspects of data from the data analyst. Such features as nonlinearity, collinearity, outliers, and points with high…

Bootstrapping a Regression Equation: Some Empirical Results

Abstract The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation based on a nonparametric estimate of the underlying error…

Bootstrapping Regression Models

Bootstrapping is a general approach to statistical inference based on building a sampling distribution for a statistic by resampling from the data at hand. The term ‘bootstrapping,’ due to Efron…

Strong consistency of least squares estimates in multiple regression.

Strong consistency of least squares estimates in multiple regression

The strong consistency of least squares estimates in multiple regression models with independent errors is obtained under minimal assumptions on the design and weak moment conditions on the errors.

Computer-Intensive Methods in Statistics

The bootstrap method is examined and evaluated as an example of this new generation of statistical tools that take advantage of the high speed digital computer and free the statistician to attack more complicated problems.

Some Asymptotic Theory for the Bootstrap

Applied Linear Regression

  • M. Malik
  • Mathematics, Computer Science
    Technometrics
  • 2005

Variance estimation in high dimensional regression models

We treat the problem of variance estimation of the least squares estimate of the parameter in high dimensional linear regression models by using the Uncor- related Weights Bootstrap ( UBS ). We find…

Part b) follows from, integration with respect to µ n , part a), and [Bickel and Freedman, 1981, Lemma 8.5] with φ(z) = vech(zz T ). The steps involving