Nonparametric maximum likelihood estimators for AR and MA time series Academic Article

abstract

• The problem of estimating the parameters of moving average or autoregressive time series is studied when the error distribution is completely unknown. Four nonparametric maximum likelihood estimators (NPMLE) are presented for this purpose. These estimators are compared with the classical moment and least squares estimators in a simulation study. The behavior of these NPMLEs is much better than the classical ones, suggesting that they should be used extensively when no parametric information is known in advance about the error distribution. An application of these estimators to coal mining accidents data is also included.

authors

• Hart, Jeffrey

published proceedings

• JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION

author list (cited authors)

• Cao, R., Hart, J. D., & Saavedra, A.

citation count

• 12

complete list of authors

• Cao, R||Hart, JD||Saavedra, A

publication date

• January 2003

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION  Journal

keywords

• Adaptive Estimation
• Dependent Data
• Kernel Estimator
• Least Squares Estimator

Digital Object Identifier (DOI)

• 10.1080/0094965021000040640

start page

• 347

end page

• 360

volume

• 73

issue

• 5

URL

• http%3A%2F%2Fdx.doi.org%2F10.1080%2F0094965021000040640

user-defined tag

• 3 Good Health and Well-Being