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Sample Out-of-Sample Inference Based on Wasserstein Distance

We present a novel inference approach that we call sample out-of-sample inference. The approach can be used widely, ranging from semisupervised learning to stress testing, and it is fundamental in the application of data-driven distributionally robust optimization. Our method enables measuring the impact of plausible out-of-sample scenarios in a given performance measure of interest, such as a financial loss. The methodology is inspired by empirical likelihood (EL), but we optimize the empirical Wasserstein distance (instead of the empirical likelihood) induced by observations. From a methodological standpoint, our analysis of the asymptotic behavior of the induced Wasserstein-distance profile function shows dramatic qualitative differences relative to EL. For instance, in contrast to EL, which typically yields chi-squared weak convergence limits, our asymptotic distributions are often not chi-squared. Also, the rates of convergence that we obtain have some dependence on the dimension in a nontrivial way but remain controlled as the dimension increases.

Ketersediaan

Informasi Detail

Judul Seri

OPERATIONS RESEARCH; Vol.69 No.3 May-June 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 985 - 1013

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NONPARAMETRIC STATISTICS
PROBABILITY
OPTIMAL TRANSPORT
DISTRIBUTIONALLY ROBUST OPTIMIZATION
WASSERSTEIN DISTANCE

Info Detail Spesifik

https://doi.org/10.1287/opre.2020.2028

Pernyataan Tanggungjawab

Jose Blanchet, Yang Kang

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