The $L_p$-error rate for randomized quasi-Monte Carlo self-normalized importance sampling of unbounded integrands (2511.10599v1)

Abstract: Self-normalized importance sampling (SNIS) is a fundamental tool in Bayesian inference when the posterior distribution involves an unknown normalizing constant. Although $L_1$-error (bias) and $L_2$-error (root mean square error) estimates of SNIS are well established for bounded integrands, results for unbounded integrands remain limited, especially under randomized quasi-Monte Carlo (RQMC) sampling. In this work, we derive $L_p$-error rate $(p\ge1)$ for RQMC-based SNIS (RQMC-SNIS) estimators with unbounded integrands on unbounded domains. A key step in our analysis is to first establish the $L_p$-error rate for plain RQMC integration. Our results allow for a broader class of transport maps used to generate samples from RQMC points. Under mild function boundary growth conditions, we further establish (L_p)-error rate of order (\mathcal{O}(N^{-β+ ε})) for RQMC-SNIS estimators, where $ε>0$ is arbitrarily small, $N$ is the sample size, and (β\in (0,1]) depends on the boundary growth rate of the resulting integrand. Numerical experiments validate the theoretical results.