Skip to main content

Differential Measurement Errors in Zero‐Truncated Regression Models for Count Data

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Summary Measurement errors in covariates may result in biased estimates in regression analysis. Most methods to correct this bias assume nondifferential measurement errors—i.e., that measurement errors are independent of the response variable. However, in regression models for zero‐truncated count data, the number of error‐prone covariate measurements for a given observational unit can equal its response count, implying a situation of differential measurement errors. To address this challenge, we develop a modified conditional score approach to achieve consistent estimation. The proposed method represents a novel technique, with efficiency gains achieved by augmenting random errors, and performs well in a simulation study. The method is demonstrated in an ecology application.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1541-0420.2011.01594.x

Affiliations: 1: Department of Mathematics, Tamkang University, New Taipei City, Taiwan 2: Institute of Statistics, National Chung Hsing University, Taichung, Taiwan

Publication date: December 1, 2011

bpl/biom/2011/00000067/00000004/art00031
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more