Instrument assisted regression for errors in variables models with binary response

Kun Xu, Yanyuan Ma, Liqun Wang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.

Original languageEnglish (US)
Pages (from-to)104-117
Number of pages14
JournalScandinavian Journal of Statistics
Volume42
Issue number1
DOIs
StatePublished - Mar 1 2015

Fingerprint

Errors-in-variables Model
Binary Response
Regression
Errors in Variables
Binary Variables
Instrumental Variables
Consistent Estimator
Generalized Linear Model
Asymptotic Properties
Simulation Study
Estimator
Prediction
Demonstrate
Errors in variables
Binary response
Generalized linear model
Simulation study
Instrumental variables
Asymptotic properties
Usefulness

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

@article{3af8de67d92f426bac809b71be8f2a7f,
title = "Instrument assisted regression for errors in variables models with binary response",
abstract = "We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.",
author = "Kun Xu and Yanyuan Ma and Liqun Wang",
year = "2015",
month = "3",
day = "1",
doi = "10.1111/sjos.12097",
language = "English (US)",
volume = "42",
pages = "104--117",
journal = "Scandinavian Journal of Statistics",
issn = "0303-6898",
publisher = "Wiley-Blackwell",
number = "1",

}

Instrument assisted regression for errors in variables models with binary response. / Xu, Kun; Ma, Yanyuan; Wang, Liqun.

In: Scandinavian Journal of Statistics, Vol. 42, No. 1, 01.03.2015, p. 104-117.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Instrument assisted regression for errors in variables models with binary response

AU - Xu, Kun

AU - Ma, Yanyuan

AU - Wang, Liqun

PY - 2015/3/1

Y1 - 2015/3/1

N2 - We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.

AB - We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.

UR - http://www.scopus.com/inward/record.url?scp=84923227753&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923227753&partnerID=8YFLogxK

U2 - 10.1111/sjos.12097

DO - 10.1111/sjos.12097

M3 - Article

C2 - 26392675

AN - SCOPUS:84923227753

VL - 42

SP - 104

EP - 117

JO - Scandinavian Journal of Statistics

JF - Scandinavian Journal of Statistics

SN - 0303-6898

IS - 1

ER -