Estimating the Index of Increase via Balancing Deterministic and Random Data

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Estimating the Index of Increase via Balancing Deterministic and Random Data. / Chen, Lingzhi; Грибкова, Надежда Викторовна; Zitikis, Ričardas; Давыдов, Юрий Александрович.

In: Mathematical Methods of Statistics, Vol. 27, No. 2, 01.04.2018, p. 83-102.

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

BibTeX

@article{bc4b517cdf89414986ee1299dbb85411,

title = "Estimating the Index of Increase via Balancing Deterministic and Random Data",

abstract = "We introduce and explore an empirical index of increase that works in both deterministic and random environments, thus allowing to assess monotonicity of functions that are prone to random measurement errors. We prove consistency of the index and show how its rate of convergence is influenced by deterministic and random parts of the data. In particular, the obtained results suggest a frequency at which observations should be taken in order to reach any pre-specified level of estimation precision.We illustrate the index using data arising from purely deterministic and error-contaminated functions, which may or may not be monotonic.",

keywords = "index of increase, determinism, randomness, measurement errors, smoothing, cross validation, cross validation, determinism, index of increase, measurement errors, randomness, smoothing, PRICE UNCERTAINTY, RISK, N-BOOTSTRAP, UTILITY, FIRM, PROSPECT-THEORY",

author = "Lingzhi Chen and Грибкова, {Надежда Викторовна} and Ri{\v c}ardas Zitikis and Давыдов, {Юрий Александрович}",

year = "2018",

month = apr,

day = "1",

doi = "10.3103/S1066530718020011",

language = "English",

volume = "27",

pages = "83--102",

journal = "Mathematical Methods of Statistics",

issn = "1066-5307",

publisher = "Allerton Press, Inc.",

number = "2",

}

RIS

TY - JOUR

T1 - Estimating the Index of Increase via Balancing Deterministic and Random Data

AU - Chen, Lingzhi

AU - Грибкова, Надежда Викторовна

AU - Zitikis, Ričardas

AU - Давыдов, Юрий Александрович

PY - 2018/4/1

Y1 - 2018/4/1

N2 - We introduce and explore an empirical index of increase that works in both deterministic and random environments, thus allowing to assess monotonicity of functions that are prone to random measurement errors. We prove consistency of the index and show how its rate of convergence is influenced by deterministic and random parts of the data. In particular, the obtained results suggest a frequency at which observations should be taken in order to reach any pre-specified level of estimation precision.We illustrate the index using data arising from purely deterministic and error-contaminated functions, which may or may not be monotonic.

AB - We introduce and explore an empirical index of increase that works in both deterministic and random environments, thus allowing to assess monotonicity of functions that are prone to random measurement errors. We prove consistency of the index and show how its rate of convergence is influenced by deterministic and random parts of the data. In particular, the obtained results suggest a frequency at which observations should be taken in order to reach any pre-specified level of estimation precision.We illustrate the index using data arising from purely deterministic and error-contaminated functions, which may or may not be monotonic.

KW - index of increase

KW - determinism

KW - randomness

KW - measurement errors

KW - smoothing

KW - cross validation

KW - determinism

KW - index of increase

KW - measurement errors

KW - randomness

KW - smoothing

KW - PRICE UNCERTAINTY

KW - RISK

KW - N-BOOTSTRAP

KW - UTILITY

KW - FIRM

KW - PROSPECT-THEORY

UR - http://www.scopus.com/inward/record.url?scp=85049859112&partnerID=8YFLogxK

U2 - 10.3103/S1066530718020011

DO - 10.3103/S1066530718020011

M3 - Article

VL - 27

SP - 83

EP - 102

JO - Mathematical Methods of Statistics

JF - Mathematical Methods of Statistics

SN - 1066-5307

IS - 2

ER -

ID: 34784842