Ridge reconstruction of partially observed functional data is
asymptotically optimal

J 2020

Ridge reconstruction of partially observed functional data is asymptotically optimal

KRAUS, David a Marco STEFANUCCI

Základní údaje

Originální název

Ridge reconstruction of partially observed functional data is asymptotically optimal

Autoři

KRAUS, David a Marco STEFANUCCI

Vydání

Statistics and Probability Letters, Amsterdam, Elsevier, 2020, 0167-7152

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 0.870

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/20:00114134

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Functional data; Partial observation; Reconstruction; Reproducing kernel Hilbert space; Ridge regularization

Štítky

14311010, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 11. 9. 2020 12:14, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

When functional data are observed on parts of the domain, it is of interest to recover the missing parts of curves. Kraus (2015) proposed a linear reconstruction method based on ridge regularization. Kneip and Liebl (2019) argue that an assumption under which Kraus (2015) established the consistency of the ridge method is too restrictive and propose a principal component reconstruction method that they prove to be asymptotically optimal. In this note we relax the restrictive assumption that the true best linear reconstruction operator is Hilbert–Schmidt and prove that the ridge method achieves asymptotic optimality under essentially no assumptions. The result is illustrated in a simulation study.

Návaznosti

GJ17-22950Y, projekt VaV

Název: Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Investor: Grantová agentura ČR, Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Citovat

KRAUS, David a Marco STEFANUCCI. Ridge reconstruction of partially observed functional data is asymptotically optimal. Statistics and Probability Letters. Amsterdam: Elsevier, 2020, roč. 165, OCT 2020, s. 1-5. ISSN 0167-7152. Dostupné z: https://doi.org/10.1016/j.spl.2020.108813.

@article{1655218,
   author = {Kraus, David and Stefanucci, Marco},
   article_location = {Amsterdam},
   article_number = {OCT 2020},
   doi = {https://doi.org/10.1016/j.spl.2020.108813},
   keywords = {Functional data; Partial observation; Reconstruction; Reproducing kernel Hilbert space; Ridge regularization},
   language = {eng},
   issn = {0167-7152},
   journal = {Statistics and Probability Letters},
   title = {Ridge reconstruction of partially observed functional data is asymptotically optimal},
   url = {https://doi.org/10.1016/j.spl.2020.108813},
   volume = {165},
   year = {2020}
}

TY  - JOUR
ID  - 1655218
AU  - Kraus, David - Stefanucci, Marco
PY  - 2020
TI  - Ridge reconstruction of partially observed functional data is asymptotically optimal
JF  - Statistics and Probability Letters
VL  - 165
IS  - OCT 2020
SP  - 1-5
EP  - 1-5
PB  - Elsevier
SN  - 01677152
KW  - Functional data
KW  - Partial observation
KW  - Reconstruction
KW  - Reproducing kernel Hilbert space
KW  - Ridge regularization
UR  - https://doi.org/10.1016/j.spl.2020.108813
L2  - https://doi.org/10.1016/j.spl.2020.108813
N2  - When functional data are observed on parts of the domain, it is of interest to recover the missing parts of curves. Kraus (2015) proposed a linear reconstruction method based on ridge regularization. Kneip and Liebl (2019) argue that an assumption under which Kraus (2015) established the consistency of the ridge method is too restrictive and propose a principal component reconstruction method that they prove to be asymptotically optimal. In this note we relax the restrictive assumption that the true best linear reconstruction operator is Hilbert–Schmidt and prove that the ridge method achieves asymptotic optimality under essentially no assumptions. The result is illustrated in a simulation study.
ER  -

KRAUS, David a Marco STEFANUCCI. Ridge reconstruction of partially observed functional data is asymptotically optimal. \textit{Statistics and Probability Letters}. Amsterdam: Elsevier, 2020, roč.~165, OCT 2020, s.~1-5. ISSN~0167-7152. Dostupné z: https://doi.org/10.1016/j.spl.2020.108813.

Přehled o publikaci