A computationally efficient correlated mixed probit model for credit risk inference

Elisa Tosetti

doi:10.1111/rssc.12352

A computationally efficient correlated mixed probit model for credit risk inference

Elisa Tosetti

Risultato della ricerca: Contributo in rivista › Articolo in rivista › peer review

Abstract

abstract

Lingua originale	English
pagine (da-a)	N/A-N/A
Rivista	JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES C, APPLIED STATISTICS
Volume	2019
DOI	https://doi.org/10.1111/rssc.12352
Stato di pubblicazione	Pubblicato - 2019
Pubblicato esternamente	Sì

Keywords

Probit model

Accesso al documento

10.1111/rssc.12352

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title = "A computationally efficient correlated mixed probit model for credit risk inference",

abstract = "abstract",

keywords = "Probit model, Probit model",

author = "Elisa Tosetti",

year = "2019",

doi = "10.1111/rssc.12352",

language = "English",

volume = "2019",

pages = "N/A--N/A",

journal = "Journal of the Royal Statistical Society. Series C: Applied Statistics",

issn = "0035-9254",

publisher = "Oxford: Blackwell.",

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TY - JOUR

T1 - A computationally efficient correlated mixed probit model for credit risk inference

AU - Tosetti, Elisa

PY - 2019

Y1 - 2019

N2 - abstract

AB - abstract

KW - Probit model

UR - http://hdl.handle.net/10807/134576

U2 - 10.1111/rssc.12352

DO - 10.1111/rssc.12352

M3 - Article

SN - 0035-9254

VL - 2019

SP - N/A-N/A

JO - Journal of the Royal Statistical Society. Series C: Applied Statistics

JF - Journal of the Royal Statistical Society. Series C: Applied Statistics

ER -

A computationally efficient correlated mixed probit model for credit risk inference

Abstract

Keywords

Accesso al documento

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Fingerprint

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