Gives the value of
$$L(\phi) = \sum_{i=1}^m w_i \phi(x_i) - \int_{x_1}^{x_m} \exp(\phi(t)) dt$$
where \(\phi\) is parametrized via
$${\bold{\eta}}({\bold{\phi}}) = \Bigl(\phi_1, \Bigl(\eta_1 + \sum_{j=2}^i (x_i-x_{i-1})\eta_i\Bigr)_{i=2}^m\Bigr).$$
Lhat_eta(x, w, eta)Value \(L({\bold{\phi}}) = L({\bold{\phi}}({\bold{\eta}}))\) of the log-likelihood function is returned.
Vector of independent and identically distributed numbers, with strictly increasing entries.
Optional vector of nonnegative weights corresponding to \({\bold{x}_m}\).
Some vector \({\bold{\eta}}\) of the same length as \({\bold{x}}\) and \({\bold{w}}\).
Kaspar Rufibach, kaspar.rufibach@gmail.com,
http://www.kasparrufibach.ch
Lutz Duembgen, duembgen@stat.unibe.ch,
https://www.imsv.unibe.ch/about_us/staff/prof_dr_duembgen_lutz/index_eng.html