Abstract
Bias reduction of a conditional maximum likelihood estimator for a Gaussian second-order moving average model
Highlights
Estimators of unknown parameters must be consistent
We show the bias of the conditional maximum likelihood estimators of unknown parameters for a Gaussian second-order moving average (MA(2)) model followed by the method in [12]
We propose a new estimator below based on the corrected bias and discuss the corrected estimators under a pure Gaussian MA(2) model in detail using the bias and the mean squared errors (MSEs) in the simulation study
Summary
Estimators of unknown parameters must be consistent. The consistency is ensured when we have large samples. We show the bias of the conditional maximum likelihood estimators of unknown parameters for a Gaussian second-order moving average (MA(2)) model followed by the method in [12]. The bias of the conditional maximum likelihood estimators of the unknown parameters given (2) for the Gaussian MA(1) model is. The conditional log-likelihood function using the finite number of εs for the Gaussian MA(2) model given (4) is expressed as. The high-order differentiations of the conditional log-likelihood function (5) are required to obtain the bias and the MSEs of the maximum likelihood estimators and Lemma 2.2 will be used for the calculations. An asymptotic expansion of the bias of the maximum likelihood estimator is given by the following: Lemma 2.3 (See Barndorff-Nielsen and Cox (1994) [5, p.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
