Robust Estimation in the Analysis of Complex Molecular Spectra
复杂分子光谱分析中的稳健估计AI 解读 ↓
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In atomic and molecular spectroscopy, measured transition energies represent differences between the energy levels of the emitting or absorbing system. In complex spectra of such transitions, the assignment of the pair of states corresponding to an observed transition energy is a difficult process. These assignments evolve in stages. At each stage, some assignments will be erroneous. Robust fitting methods have proven extremely helpful for finding gross errors and for obtaining reliable estimates of energy levels in the analysis of molecular tritium (T 2) spectra. The robust results can be used successfully to obtain new assignments of transition energies to energy levels. In statistical terms, a highly unbalanced two-way analysis of variance model results. Some specific robustness problems that call for further research are mentioned. An iterative algorithm that is fast and avoids storage problems, even for large data sets, is described. This algorithm can be used for both least squares and robust fitting of incomplete two-way tables and similar designs.
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Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a new test that leverages generative models to compare conditional distributions using both observed data and synthetic samples. A sample-splitting and studentization scheme is introduced to control estimation error. Under the null, the test…
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Normality pre-tests on reduced-form innovations cannot detect whether two or more underlying structural shocks are Gaussian, and hence cannot detect a failure of identification in non-Gaussian SVARs. We propose a bootstrap-based approach that reveals such a failure. Under the null of valid identification and certain regularity conditions, the conditional bootstrap distribution of the maximum likelihood estimator of the impact matrix is asymptotically normal, so the…
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We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of naturally paired high-dimensional test statistics and the associated $p$-values for evaluating the same null hypotheses. The first set is shown to be asymptotically mutually independent,…
Principal stratification (PS) is a commonly used approach for understanding the mechanisms through which a treatment affects an outcome. The goal of this work is to extend the PS framework to studies with continuous treatments, which introduces a number of both challenges and opportunities in terms of defining causal effects and performing inference. This manuscript provides multiple key methodological contributions: 1) we introduce principal causal estimands for continuous…