Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
Schervish
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 7roles
background 2polarities
background 2representative citing papers
Derives the asymptotic distribution of the spatial Cramér-von Mises independence statistic under β-mixing on R² and implements it in Python with eigenvalue-based critical values.
Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.
Non-affine approval functions create unavoidable miscalibration in proper scoring rules for strategic agents, but step-function thresholds enable first-best screening without it, uniquely for the Brier score.
An iterated I-projection procedure solves the generalized minimum information checkerboard copula problem with convergence guarantees and numerical tests up to dimension four.
A data-driven method adaptively selects the number of LLM-simulated responses to form confidence sets with nominal coverage for human survey parameters and equates that number to the LLM's effective human-equivalent sample size.
A review summarizing mathematical foundations, characterization results, families of proper scoring rules, and their roles in statistics and machine learning for estimation and forecast evaluation.
citing papers explorer
-
Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
-
The Spatial Cram'{e}r--von Mises Test of Independence under $\beta$-Mixing: Asymptotic Theory and Python Implementation
Derives the asymptotic distribution of the spatial Cramér-von Mises independence statistic under β-mixing on R² and implements it in Python with eigenvalue-based critical values.
-
Primitive Sequences for Probability Measures on Compact Intervals
Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.
-
The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting
Non-affine approval functions create unavoidable miscalibration in proper scoring rules for strategic agents, but step-function thresholds enable first-best screening without it, uniquely for the Brier score.
-
An iterated $I$-projection procedure for solving the generalized minimum information checkerboard copula problem
An iterated I-projection procedure solves the generalized minimum information checkerboard copula problem with convergence guarantees and numerical tests up to dimension four.
-
How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
A data-driven method adaptively selects the number of LLM-simulated responses to form confidence sets with nominal coverage for human survey parameters and equates that number to the LLM's effective human-equivalent sample size.
-
Proper scoring rules for estimation and forecast evaluation
A review summarizing mathematical foundations, characterization results, families of proper scoring rules, and their roles in statistics and machine learning for estimation and forecast evaluation.