  Motivated by modeling and analysis of mass-spectrometry data, a semi- and
nonparametric model is proposed that consists of a linear parametric component
for individual location and scale and a nonparametric regression function for
the common shape. A multi-step approach is developed that simultaneously
estimates the parametric components and the nonparametric function. Under
certain regularity conditions, it is shown that the resulting estimators is
consistent and asymptotic normal for the parametric part and achieve the
optimal rate of convergence for the nonparametric part when the bandwidth is
suitably chosen. Simulation results are presented to demonstrate the
effectiveness and finite-sample performance of the method. The method is also
applied to a SELDI-TOF mass spectrometry data set from a study of liver cancer
patients.
