Abstract
It is often crucial to make inferences about parameters of a nonlinear regression model due to a dependency of Fisher information on the parameter being estimated. Here, the distribution of the relevant test statistic is not exact, but approximate. Therefore, similar conclusion, based on the values of different test statistics, may not be reached. This study shows, in this circumstance, how to come up with a nonlinear regression model that can be used for forecasting and other related purposes. The goodness of the approximate distributions, F and χ 2 , has been assessed to reach a correct decision. The simulation results show that the simulated probability of committing a type I error is very close to its true value in case of F distribution corresponding to F statistic. However, the χ 2 distribution does not do a similar job for the LRT statistic since the simulated type I error is quite larger. Dhaka Univ. J. Sci. 71(1): 13-16, 2023 (Jan)
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.
