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Directing Power Towards Conic Parameter Subspaces
Statistics Theory · Submitted 11 Jul 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim I simultaneously address these two issues by proposing a novel test statistic that is large in a conic parameter subspace of interest.
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The abstract, sentence by sentence
For a high-dimensional parameter of interest, tests based on quadratic statistics are known to have low power against subsets of the parameter space (henceforth, parameter subspaces). In addition, they typically involve an inverse covariance matrix which is difficult to estimate in high-dimensional settings. I simultaneously address these two issues by proposing a novel test statistic that is large in a conic parameter subspace of interest. This test statistic generalizes the Wald statistic and nests many well-known test statistics. For a given parameter subspace, the statistic is free of tuning parameters and suitable for high-dimensional settings if the subspace is sufficiently small. It can be computed using regularized linear regression, where the type of regularization and the regularization parameters are completely determined by the parameter subspace of interest. I illustrate the statistic on subspaces that consist of sparse or nearly-sparse vectors, for which the computation corresponds to $\ell_0$- and $\ell_1$-regularized regression, respectively.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | For a high-dimensional parameter of interest, tests based on quadratic statistics are known to have low power against subsets of the parameter space (henceforth, parameter subspaces). | background 0.99 | 0.04 | 0.05 | asserted 0.99 |
| s1 | In addition, they typically involve an inverse covariance matrix which is difficult to estimate in high-dimensional settings. | background 0.95 | 0.04 | 0.02 | asserted 0.87 |
| s2 | I simultaneously address these two issues by proposing a novel test statistic that is large in a conic parameter subspace of interest. | contribution 1.00 | 0.93 | 0.55 | asserted 1.00 |
| s3 | This test statistic generalizes the Wald statistic and nests many well-known test statistics. | contribution 0.71 | 0.79 | 0.10 | asserted 1.00 |
| s4 | For a given parameter subspace, the statistic is free of tuning parameters and suitable for high-dimensional settings if the subspace is sufficiently small. | method 0.36 | 0.77 | 0.17 | asserted 0.92 |
| s5 | It can be computed using regularized linear regression, where the type of regularization and the regularization parameters are completely determined by the parameter subspace of interest. | method 0.99 | 0.79 | 0.09 | asserted 0.97 |
| s6 | I illustrate the statistic on subspaces that consist of sparse or nearly-sparse vectors, for which the computation corresponds to $\ell_0$- and $\ell_1$-regularized regression, respectively. | method 0.50 | 0.85 | 0.06 | asserted 1.00 |
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The paper
Original abstract
For a high-dimensional parameter of interest, tests based on quadratic statistics are known to have low power against subsets of the parameter space (henceforth, parameter subspaces). In addition, they typically involve an inverse covariance matrix which is difficult to estimate in high-dimensional settings. I simultaneously address these two issues by proposing a novel test statistic that is large in a conic parameter subspace of interest. This test statistic generalizes the Wald statistic and nests many well-known test statistics. For a given parameter subspace, the statistic is free of tuning parameters and suitable for high-dimensional settings if the subspace is sufficiently small. It can be computed using regularized linear regression, where the type of regularization and the regularization parameters are completely determined by the parameter subspace of interest. I illustrate the statistic on subspaces that consist of sparse or nearly-sparse vectors, for which the computation corresponds to $\ell_0$- and $\ell_1$-regularized regression, respectively.
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The $T_k$ statistic is computed using the methodology proposed by [ref] .
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms · Tests · 1.00
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