Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
Abstract
Fitting high-dimensional statistical models often requires the use of non-linear parameter estimation procedures. As a consequence, it is generally impossible to obtain an exact characterization of the probability distribution of the parameter estimates. This in turn implies that it is extremely challenging to quantify the uncertainty associated with a certain parameter estimate. Concretely, no commonly accepted procedure exists for computing classical measures of uncertainty and statistical significance as confidence intervals or $p$-values for these models.
Cite
Text
Javanmard and Montanari. "Confidence Intervals and Hypothesis Testing for High-Dimensional Regression." Journal of Machine Learning Research, 2014.Markdown
[Javanmard and Montanari. "Confidence Intervals and Hypothesis Testing for High-Dimensional Regression." Journal of Machine Learning Research, 2014.](https://mlanthology.org/jmlr/2014/javanmard2014jmlr-confidence/)BibTeX
@article{javanmard2014jmlr-confidence,
title = {{Confidence Intervals and Hypothesis Testing for High-Dimensional Regression}},
author = {Javanmard, Adel and Montanari, Andrea},
journal = {Journal of Machine Learning Research},
year = {2014},
pages = {2869-2909},
volume = {15},
url = {https://mlanthology.org/jmlr/2014/javanmard2014jmlr-confidence/}
}