Exact model comparisons in the plausibility framework

نویسندگان

چکیده

Plausibility is a formalization of exact tests for parametric models and generalizes procedures such as Fisher's test. The resulting are based on cumulative probabilities the probability density function evaluate consistency with family while providing control $\alpha$ level finite sample size. Model comparisons inefficient in this approach. We generalize plausibility by incorporating weighing which allows to perform model comparisons. show that one scheme asymptotically equivalent likelihood ratio test (LRT) has guarantees size under null hypothesis unlike LRT. confirm theoretical properties simulations mimic data set our application. apply method retinoblastoma demonstrate parent-of-origin effect. Weighted also applications high-dimensional analysis P-values penalized regression can be derived. superior performance compared data-splitting procedure simulation study. weighted gene expression, case-control prostate cancer set. discuss flexibility approach relating targeted learning, bootstrap, sparsity selection.

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ژورنال

عنوان ژورنال: Journal of Statistical Planning and Inference

سال: 2021

ISSN: ['1873-1171', '0378-3758']

DOI: https://doi.org/10.1016/j.jspi.2021.07.013