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On distance in total variation between image measures. / Davydov, Youri.

In: Statistics and Probability Letters, Vol. 129, 10.2017, p. 393-400.

Research output: Contribution to journal › Article › peer-review

Harvard

Davydov, Y 2017, 'On distance in total variation between image measures', Statistics and Probability Letters, vol. 129, pp. 393-400. https://doi.org/10.1016/j.spl.2017.06.022

APA

Davydov, Y. (2017). On distance in total variation between image measures. Statistics and Probability Letters, 129, 393-400. https://doi.org/10.1016/j.spl.2017.06.022

Vancouver

Davydov Y. On distance in total variation between image measures. Statistics and Probability Letters. 2017 Oct;129:393-400. https://doi.org/10.1016/j.spl.2017.06.022

Author

Davydov, Youri. / On distance in total variation between image measures. In: Statistics and Probability Letters. 2017 ; Vol. 129. pp. 393-400.

BibTeX

@article{485e66123a804baabaea084aee63a14f,
title = "On distance in total variation between image measures",
abstract = "We are interested in the estimation of the distance in total variation Δ≔‖Pf(X)−Pg(X)‖varbetween distributions of random variables f(X) and g(X) in terms of proximity of f and g. We propose a simple general method of estimating Δ. For Gaussian and trigonometrical polynomials it gives an asymptotically optimal result (when the degree tends to ∞).",
keywords = "Gaussian polynomials, Image-measures, Nikol'ski–Besov class, Total variation distance",
author = "Youri Davydov",
year = "2017",
month = oct,
doi = "10.1016/j.spl.2017.06.022",
language = "English",
volume = "129",
pages = "393--400",
journal = "Statistics and Probability Letters",
issn = "0167-7152",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - On distance in total variation between image measures

AU - Davydov, Youri

PY - 2017/10

Y1 - 2017/10

N2 - We are interested in the estimation of the distance in total variation Δ≔‖Pf(X)−Pg(X)‖varbetween distributions of random variables f(X) and g(X) in terms of proximity of f and g. We propose a simple general method of estimating Δ. For Gaussian and trigonometrical polynomials it gives an asymptotically optimal result (when the degree tends to ∞).

AB - We are interested in the estimation of the distance in total variation Δ≔‖Pf(X)−Pg(X)‖varbetween distributions of random variables f(X) and g(X) in terms of proximity of f and g. We propose a simple general method of estimating Δ. For Gaussian and trigonometrical polynomials it gives an asymptotically optimal result (when the degree tends to ∞).

KW - Gaussian polynomials

KW - Image-measures

KW - Nikol'ski–Besov class

KW - Total variation distance

UR - http://www.scopus.com/inward/record.url?scp=85027453620&partnerID=8YFLogxK

U2 - 10.1016/j.spl.2017.06.022

DO - 10.1016/j.spl.2017.06.022

M3 - Article

AN - SCOPUS:85027453620

VL - 129

SP - 393

EP - 400

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

SN - 0167-7152

ER -

ID: 49897121