We present an integrated optimization approach to parameter estimation for discrete choice
demand models where data for one or more choice alternatives are censored. We employ a
mixed-integer program (MIP) to jointly determine the prediction parameters associated with
the customer arrival rate and their substitutive choices. This integrated approach enables us
to recover proven,(near-) optimal parameter values with respect to the chosen loss-
minimization (LM) objective function, thereby overcoming a limitation of prior multistart …

We present an integrated mixed-integer programming (MIP) approach to parameter
estimation for discrete choice demand models where data for one or more choice
alternatives are censored. We jointly determine the prediction parameters associated with a
time-varying customer arrival rate and their substitutive choices and recover (near-) optimal
parameter values with respect to the chosen loss-minimization objective. We propose a dual-
layer estimation model extension that learns the unobserved market shares of competitors …