Approximating Fisher’s Information for the Replicated Linear Circular Functional Relationship Model

The problem that this paper attempting to solve is the derivation of Fisher’s information matrix using four parameters which are two error concentration parameters of variables, intercept and slope parameter for the replicated linear circular functional relationship model. The model is formulated assuming both variables are circular, subject to errors and there is a linear relationship between them. The maximum likelihood estimation have been used to estimate all the parameters. It is shown that estimate of Fisher’s information can be obtained by using various theories of matrices and approximation of the asymptotic properties of Bassel function.