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Measurement Error in Dynamic Models
| Content Provider | Scilit |
|---|---|
| Author | Buonaccorsi, John P. |
| Copyright Year | 2021 |
| Description | The fitting of time series models is of interest in many disciplines, including its use in modeling the dynamics of human and animal populations, air pollution, temperatures, disease rates, labor and economic indices, etc. As with many other types of data, time series data are prone to measurement/observation error since the main variable of interest frequently needs to be estimated at each point in time. The goal of this chapter is an overview of fitting linear and non-linear autoregressive models that account for these measurement errors as well as assessing the effects of the “naive” approach of ignoring the measurement error. We survey the literature and outline various fitting strategies including moment and regression type approaches and likelihood based methods. The methods are illustrated with two examples; the first fits a linear autoregressive model to mallard duck populations while the second fits the popular non-linear Ricker model to moose abundance data. Book Name: Handbook of Measurement Error Models |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781315101279-18&type=chapterpdf |
| Ending Page | 402 |
| Page Count | 24 |
| Starting Page | 379 |
| DOI | 10.1201/9781315101279-18 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-09-28 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Handbook of Measurement Error Models Measurement Error Survey Interest Fitting Linear Linear Autoregressive Autoregressive Models Modeling the Dynamics |
| Content Type | Text |
| Resource Type | Chapter |
