53 citations · 65 across the 13 of their papers we have counts for
13 papers
Mitigating the Winner's Curse While Controlling Multiplicity: e-Process Methods for Anytime-Valid Inference in Dose-Ranging Trials
Victor K. de la Pena, Fangyuan Lin, Demissie Alemayehu +1
Phase II dose-ranging trials often report the largest observed dose-control effect while inspecting accumulating data repeatedly. This creates two coupled distortions: selection op…
The Exact Ville Identity: From the Absorbing Case to the General Law with an Application to E-Values
Victor H. de la Pena, Michael J. Klass
For a nonnegative supermartingale with , let be the first time it reaches a level . Ville's inequality gives only the bound …
Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty
Fangyuan Lin, Spencer Frei, Victor H. de la Pena
We consider the concentration properties of functions of infinitely exchangeable random variables. By conditioning on the de Finetti directing measure, we show that the deviation o…
Beyond Wald's Equation and the Optional Sampling Theorem
Michael J. Klass, Victor H. de la Pena
This paper establishes a conservation identity for mean-zero martingales stopped by extended-valued stopping times. For any mean-zero martingale and any extended-valued s…
Sharp Decoupling Inequalities for the Variances and Second Moments of Sums of Dependent Random Variables
Victor H. de la Pena, Heyuan Yao, Demissie Alemayehu
Both complete decoupling and tangent decoupling are classical tools aiming to compare two random processes where one has a weaker dependence structure. We give a new proof for the…
Prediction and estimation of random variables with infinite mean or variance
Victor de la Pena, Henryk Gzyl, Silvia Mayoral +2
In this paper we propose an optimal predictor of a random variable that has either an infinite mean or an infinite variance. The method consists of transforming the random variable…
On the relationship between beta-Bartlett and Uhlig extended processes
Víctor Peña, Kaoru Irie
Stochastic volatility processes are used in multivariate time-series analysis to track time-varying patterns in covariance matrices. Uhlig extended and beta-Bartlett processes are…
Sharp Concentration Results for Heavy-Tailed Distributions
Milad Bakhshizadeh, Arian Maleki, Victor H. de la Pena
We obtain concentration and large deviation for the sums of independent and identically distributed random variables with heavy-tailed distributions. Our concentration results are…
From Boundary Crossing of Non-Random Functions to Boundary Crossing of Stochastic Processes
Mark Brown, Victor de la Pena, Tony Sit
One problem of wide interest involves estimating expected crossing-times. Several tools have been developed to solve this problem beginning with the works of Wald and the theory of…
Characterizations of joint distributions, copulas, information, dependence and decoupling, with applications to time series
Victor H. de la Peña, Rustam Ibragimov, Shaturgun Sharakhmetov
In this paper, we obtain general representations for the joint distributions and copulas of arbitrary dependent random variables absolutely continuous with respect to the product o…
Contraction and decoupling inequalities for multilinear forms and u-statistics
V. de la Pena, Stephen J. Montgomery-Smith, Jerzy Szulga
We prove decoupling inequalities for random polynomials in independent random variables with coefficients in vector space. We use various means of comparison, including rearrangeme…
Bounds on the tail probability of U-statistics and quadratic forms
Victor H. de la Peña, Stephen J. Montgomery-Smith
The authors announce a general tail estimate, called a decoupling inequality, for a symmetrized sum of non-linear -correlations of independent random variables.
Decoupling Inequalities for the Tail Probabilities of Multivariate U-statistics
Victor H. de la Peña, Stephen J. Montgomery-Smith
In this paper the following result, which allows one to decouple U-Statistics in tail probability, is proved in full generality. Theorem 1. Let be a sequence of independent r…