arXiv subjects

Nick Koning

Publications and source records attributed to Nick Koning.

3 recordsLinked to original sources

arXiv2019

Directing Power Towards Conic Parameter Subspaces

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.

math.ST↗

arXiv2019

Sparse Unit-Sum Regression

This paper considers sparsity in linear regression under the restriction that the regression weights sum to one. We propose an approach that combines $\ell_0$- and $\ell_1$-regularization. We compute its solution by adapting a recent methodological innovation made by Bertsimas et al. (2016) for $\ell_0$-regularization in standard linear regression. In a simulation experiment we compare our approach to $\ell_0$-regularization and $\ell_1$-regularization and find that it performs favorably in terms of predictive performance and sparsity. In an application to index tracking we show that our approach can obtain substantially sparser portfolios compared to $\ell_1$-regularization while maintaining a similar tracking performance.

stat.ME↗

arXiv2019

Exact Testing of Many Moment Inequalities Against Multiple Violations

This paper considers the problem of testing many moment inequalities, where the number of moment inequalities ($p$) is possibly larger than the sample size ($n$). Chernozhukov et al. (2019) proposed asymptotic tests for this problem using the maximum $t$ statistic. We observe that such tests can have low power if multiple inequalities are violated. As an alternative, we propose novel randomization tests based on a maximum non-negatively weighted combination of $t$ statistics. We provide a condition guaranteeing size control in large samples. Simulations show that the tests control size in small samples ($n = 30$, $p = 1000$), and often has substantially higher power against alternatives with multiple violations than tests based on the maximum $t$ statistic.

math.ST↗