Statistical Inference of Optimal Allocations I: Regularities and their Implications
- URL: http://arxiv.org/abs/2403.18248v2
- Date: Sun, 7 Apr 2024 08:40:50 GMT
- Title: Statistical Inference of Optimal Allocations I: Regularities and their Implications
- Authors: Kai Feng, Han Hong,
- Abstract summary: We first derive Hadamard differentiability of the value function through a detailed analysis of the general properties of the sorting operator.
Building on our Hadamard differentiability results, we demonstrate how the functional delta method can be used to directly derive the properties of the value function process.
- Score: 3.904240476752459
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we develop a functional differentiability approach for solving statistical optimal allocation problems. We first derive Hadamard differentiability of the value function through a detailed analysis of the general properties of the sorting operator. Central to our framework are the concept of Hausdorff measure and the area and coarea integration formulas from geometric measure theory. Building on our Hadamard differentiability results, we demonstrate how the functional delta method can be used to directly derive the asymptotic properties of the value function process for binary constrained optimal allocation problems, as well as the two-step ROC curve estimator. Moreover, leveraging profound insights from geometric functional analysis on convex and local Lipschitz functionals, we obtain additional generic Fr\'echet differentiability results for the value functions of optimal allocation problems. These compelling findings motivate us to study carefully the first order approximation of the optimal social welfare. In this paper, we then present a double / debiased estimator for the value functions. Importantly, the conditions outlined in the Hadamard differentiability section validate the margin assumption from the statistical classification literature employing plug-in methods that justifies a faster convergence rate.
Related papers
- Statistical Inference for Temporal Difference Learning with Linear Function Approximation [62.69448336714418]
Temporal Difference (TD) learning, arguably the most widely used for policy evaluation, serves as a natural framework for this purpose.
In this paper, we study the consistency properties of TD learning with Polyak-Ruppert averaging and linear function approximation, and obtain three significant improvements over existing results.
arXiv Detail & Related papers (2024-10-21T15:34:44Z) - Kernel-based off-policy estimation without overlap: Instance optimality
beyond semiparametric efficiency [53.90687548731265]
We study optimal procedures for estimating a linear functional based on observational data.
For any convex and symmetric function class $mathcalF$, we derive a non-asymptotic local minimax bound on the mean-squared error.
arXiv Detail & Related papers (2023-01-16T02:57:37Z) - Statistical Optimality of Divide and Conquer Kernel-based Functional
Linear Regression [1.7227952883644062]
This paper studies the convergence performance of divide-and-conquer estimators in the scenario that the target function does not reside in the underlying kernel space.
As a decomposition-based scalable approach, the divide-and-conquer estimators of functional linear regression can substantially reduce the algorithmic complexities in time and memory.
arXiv Detail & Related papers (2022-11-20T12:29:06Z) - Off-policy estimation of linear functionals: Non-asymptotic theory for
semi-parametric efficiency [59.48096489854697]
The problem of estimating a linear functional based on observational data is canonical in both the causal inference and bandit literatures.
We prove non-asymptotic upper bounds on the mean-squared error of such procedures.
We establish its instance-dependent optimality in finite samples via matching non-asymptotic local minimax lower bounds.
arXiv Detail & Related papers (2022-09-26T23:50:55Z) - Data-Driven Influence Functions for Optimization-Based Causal Inference [105.5385525290466]
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing.
We study the case where probability distributions are not known a priori but need to be estimated from data.
arXiv Detail & Related papers (2022-08-29T16:16:22Z) - From Majorization to Interpolation: Distributionally Robust Learning
using Kernel Smoothing [1.2891210250935146]
We study the function approximation aspect of distributionally robust optimization (DRO) based on probability metrics.
This paper instead proposes robust learning algorithms based on smooth function approximation and convolution.
arXiv Detail & Related papers (2021-02-16T22:25:18Z) - Optimal oracle inequalities for solving projected fixed-point equations [53.31620399640334]
We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space.
We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation.
arXiv Detail & Related papers (2020-12-09T20:19:32Z) - Equivalence of Convergence Rates of Posterior Distributions and Bayes
Estimators for Functions and Nonparametric Functionals [4.375582647111708]
We study the posterior contraction rates of a Bayesian method with Gaussian process priors in nonparametric regression.
For a general class of kernels, we establish convergence rates of the posterior measure of the regression function and its derivatives.
Our proof shows that, under certain conditions, to any convergence rate of Bayes estimators there corresponds the same convergence rate of the posterior distributions.
arXiv Detail & Related papers (2020-11-27T19:11:56Z) - SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for
Gaussian Process Regression with Derivatives [86.01677297601624]
We propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features.
We prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior.
arXiv Detail & Related papers (2020-03-05T14:33:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.