 Modified profile likelihood for exponential regression

For independent exponential distributions with means $\lambda e^{\psi x_i}$ and a nonconstant fixed design, put $A(\psi )={\sum }_i{Y}_i{e}^{-\psi x_i}$. The constrained maximum-likelihood estimate is ${\tilde{\lambda }}_{\psi }=A(\psi )/n$ and the profile log-likelihood is ${\ell }_p(\psi )=-n\log [A(\psi )/n]-\psi {\sum }_i{x}_i-n$. Use the ancillary statistic $a_i=\log Y_i-\log \hat{\lambda }-\hat{\psi }{x}_i$ to specify sample coordinates. At fixed $\hat{\psi },a$, the Jacobian determinant $\partial {\tilde{\lambda }}_{\psi }/\partial \hat{\lambda }={\tilde{\lambda }}_{\psi }/\hat{\lambda }$, while $j_{\lambda \lambda }(\psi ,{\tilde{\lambda }}_{\psi })=n/{\tilde{\lambda }}_{\psi }^2$. Therefore the modified profile likelihood satisfiesThe adjustment is independent of $\psi$. Keeping only the observed information adjustment would miss this cancellation.