  Measurement error is an important problem that has not been very well studied
in the context of Functional Data Analysis. To the best of our knowledge, there
are no existing methods that address the presence of functional measurement
errors in generalized functional linear models. A framework is proposed for
estimating the slope function in the presence of measurement error in the
generalized functional linear model with a scalar response. This work extends
the conditional-score method to the case when both the measurement error and
the independent variables lie in an infinite dimensional space. Asymptotic
results are obtained for the proposed estimate and its behavior is studied via
simulations, when the response is continuous or binary. It's performance on
real data is demonstrated through a simulation study based on the Canadian
Weather data-set, where errors are introduced in the data-set and it is
observed that the proposed estimate indeed performs better than a naive
estimate that ignores the measurement error.
