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MISE ofˆβofˆ ofˆβ(t) as J increases, for a logistic model with estimated imputation parameters.

MISE ofˆβofˆ ofˆβ(t) as J increases, for a logistic model with estimated imputation parameters.

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Figure 3: Imputed curves for each of the four imputation approaches....
MISE ofˆβofˆ ofˆβ(t) as J increases, for a logistic model with...
MISE as m increases, using true parameters, with J = 3.
Highly Irregular Functional Generalized Linear Regression with Electronic Health Records
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  • Aug 2019
This work presents a new approach, called MISFIT, for fitting generalized functional linear regression models with sparsely and irregularly sampled data. Current methods do not allow for consistent estimation unless one assumes that the number of observed points per curve grows sufficiently quickly with the sample size. In contrast, MISFIT is base...
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