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One approach to functional data analysis uses generalized additive models to estimate functions of predictor variables, adding together each effect to predict the response variable.
However, if we would like to estimate functions of the form ∏ ( x p j − 1 ).
Controlling errors of distance estimate functions and location decision functions is very challenging.
As far as we know, distance estimate functions built locally by landmarks differ only in their parameters and not in their forms, i.e. all distance estimate functions used by landmarks may be linear, polynomial and so on, but without mix.
We are going to use triple operator integrals to estimate functions of pairs of noncommuting self-adjoint operators under perturbation.
So we consider the setting of nonparametric statistics, which is designed to estimate functions when no parametric model can be assumed.
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Now, we estimate function defined by (2.61).
The latter assumes "nearly perfect" estimate function and measurements.
Selecting the optimal estimate function is still a challenge, which is out of the scope of this paper.
Differential gene expression was determined using the cuffdiff maximum likelihood estimate function.
We fitted a survival curve for each molecular group of Gbs using the Kaplan Meier estimate (function survfit).
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