Let θ = (θ1, . . . . θp) * with θ* being the final iteration for θ.
The likelihood-based 100 (1 - α) % confidence limits satisfy:
where S( θp ) is the SSE obtained when holding θp fixed and minimizing over the other parameters.1 This is equivalent to solving:
S(θp) = S(θ*) + (tα/2)2 MSE
|N||total number of observations|
|P||number of free (unlocked) parameters|
|tα/2||upper α/2 point of the t distribution with N - P degrees of freedom|
|S(θ)||Sum of the squared error|
|MSE||mean squared error|
- Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. John Wiley & Sons, Inc.