Constrained inference in linear regression

@article{Peiris2016ConstrainedII,
  title={Constrained inference in linear regression},
  author={Thelge Buddika Peiris and Bhaskar Bhattacharya},
  journal={J. Multivar. Anal.},
  year={2016},
  volume={151},
  pages={133-150}
}
Inference in Constrained Linear Regression
TLDR
The restricted prediction interval for the mean of the regression function is constructed when two predictors are present and a modified likelihood ratio test (LRT) is used to construct prediction intervals.
Likelihood Inference for Order Restricted Models
TLDR
This thesis reviews the articles that focused in the behaviour of the Likelihood methods on Order restricted models and concludes likelihood inference is satisfactory for simple order cone restriction, but it is unsatisfactory when the restrictions are of the tree order, umbrella order, star-shaped and stochastic order types.
Comparison Between Confidence Intervals of Multiple Linear Regression Model with or without Constraints
Regression analysis is one of the most applied statistical techniques. The statistical inference of a linear regression model with a monotone constraint had been discussed in early analysis. A

References

SHOWING 1-10 OF 26 REFERENCES
Order-restricted inferences in linear regression
Abstract Regression analysis constitutes a large portion of the statistical repertoire in applications. In cases where such analysis is used for exploratory purposes with no previous knowledge of the
Sign-constrained least squares estimation for high-dimensional regression
TLDR
Network tomography is shown to be an application where the necessary conditions for success of non-negative least squares are naturally fulfilled and empirical results confirm the effectiveness of the sign constraint for sparse recovery.
Applied Linear Statistical Models
TLDR
Applied Linear Statistical Models 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
Order restricted statistical inference
Isotonic Regression. Tests of Ordered Hypotheses: The Normal Means Case. Tests of Ordered Hypotheses: Generalizations of the Likelihood Ratio Tests and Other Procedures. Inferences about a Set of
Better subset regression using the nonnegative garrote
A new method, called the nonnegative (nn) garrote, is proposed for doing subset regression. It both shrinks and zeroes coefficients. In tests on real and simulated data, it produces lower prediction
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
TLDR
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
Constrained Statistical Inference: Inequality, Order, and Shape Restrictions
Dedication. Preface. 1. Introduction. 1.1 Preamble. 1.2 Examples. 1.3 Coverage and Organization of the Book. 2. Comparison of Population Means and Isotonic Regression. 2.1 Ordered Hypothesis
Nonnegativity constraints in numerical analysis
A survey of the development of algorithms for enforcing nonnegativity constraints in scientific computation is given. Special emphasis is placed on such constraints in least squares computations in
Covariance-Preconditioned Iterative Methods for Nonnegatively Constrained Astronomical Imaging
TLDR
Each of the methods presented can be viewed as an implementation of a preconditionsed modified residual-norm steepest descent algorithm with a specific preconditioner, and it is shown that the well-known and often used Richardson-Lucy algorithm is one such method.
...
...