SKEWED BERNSTEIN-VON MISES THEOREM AND SKEW-MODAL APPROXIMATIONS

• 成果类型: Article
• 署名作者: Durante, Daniele; Pozza, Francesco; Szabo, Botond
• 署名单位: Bocconi University; Bocconi University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/24-AOS2429
• 发表日期: 2024
• 页码: 2714-2737
• 关键词: variational inference; Edgeworth Expansion; bayesian-inference; regression

摘要

Gaussian deterministic approximations are routinely employed in Bayesian statistics to ease inference when the target posterior is intractable. While these approximations are justified, in asymptotic regimes, by Bernstein-von Mises type results, in practice the expected Gaussian behavior might poorly represent the actual shape of the target posterior, thus affecting approximation accuracy. Motivated by these considerations, we derive an improved class of closed-form and valid deterministic approximations of posterior distributions that arise from a novel treatment of a third-order version of the Laplace method yielding approximations within a tractable family of skew-symmetric distributions. Under general assumptions accounting for misspecified models and non-i.i.d. settings, such a family of approximations is shown to have a total variation distance from the target posterior whose convergence rate improves by at least one order of magnitude the one achieved by the Gaussian from the classical Bernstein-von Mises theorem. Specializing this result to the case of regular parametric models shows that the same accuracy improvement can be also established for the posterior expectation of polynomially bounded functions. Unlike available higher-order approximations based on, for example, Edgeworth expansions, our results prove that it is possible to derive closed-form and valid densities which provide a more accurate, yet similarly tractable, alternative to Gaussian approximations of the target posterior, while inheriting its limiting frequentist properties. We strengthen these arguments by developing a practical skew-modal approximation for both joint and marginal posteriors which preserves the guarantees of its theoretical counterpart by replacing the unknown model parameters with the corresponding maximum a posteriori estimate. Simulation studies and real-data applications confirm that our theoretical results closely match the empirical gains observed in practice.