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