Inflation of Type I error rate in multiple regression when independent variables are measured with error | Zendy

Brunner Jerry | Zendy; Austin Peter C. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Inflation of Type I error rate in multiple regression when independent variables are measured with error

Author(s) -

Brunner Jerry,

Austin Peter C.

Publication year - 2009

Publication title -

canadian journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.804

H-Index - 51

eISSN - 1708-945X

pISSN - 0319-5724

DOI - 10.1002/cjs.10004

Subject(s) - type i and type ii errors , statistics , mathematics , ordinary least squares , regression analysis , inflation (cosmology) , monte carlo method , observational error , regression , variables , scale (ratio) , regression diagnostic , linear regression , econometrics , logistic regression , polynomial regression , physics , quantum mechanics , theoretical physics

When independent variables are measured with error, ordinary least squares regression can yield parameter estimates that are biased and inconsistent. This article documents an inflation of Type I error rate that can also occur. In addition to analytic results, a large‐scale Monte Carlo study shows unacceptably high Type I error rates under circumstances that could easily be encountered in practice. A set of smaller‐scale simulations indicate that the problem applies to various types of regression and various types of measurement error. The Canadian Journal of Statistics 37: 33‐46; 2009 © 2009 Statistical Society of Canada

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research