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Identifiability in Measurement Error Models
| Content Provider | Scilit |
|---|---|
| Author | Wang, Liqun |
| Copyright Year | 2021 |
| Description | Identifiability is a fundamental issue in measurement error models, because consistent estimation of all unknown parameters in the model is impossible if it is unidentifiable. In this Chapter we study this issue in linear and nonlinear regression models with either classical or Berkson type measurement error. We use a simple linear model to demonstrate how the measurement error in covariates causes its non-identifiability. We show that the nonlinear models with Berkson measurement error can be identified without prior restrictions on the parameters or extra data besides the main sample. We also show that the classical measurement error (errors in variables) models can be identified using the instrumental variable approach and provide a sufficient rank condition for the identifiability. Some examples are provided for illustration. Book Name: Handbook of Measurement Error Models |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781315101279-3&type=chapterpdf |
| Ending Page | 70 |
| Page Count | 16 |
| Starting Page | 55 |
| DOI | 10.1201/9781315101279-3 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-09-28 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Handbook of Measurement Error Models Nonlinear Berkson Models Can Be Identified Identifiability Measurement Error Models |
| Content Type | Text |
| Resource Type | Chapter |
