On developed estimation methods via unique and multiple parametrization

In the present study, two methods, based on entropy optimization principle, to estimate missing (or forecasting) values in the given time series are suggested. In the first method successively replaces the missing values with the parameter value minimizing entropy of multivariate normal distribution representing MaxEnt approximation of the time series which arises by parameterization with a single parameter. In the second method, all missing values are parameterized with multiple parameters for each missing value and are estimated at a time. These methods are applied to biomedical data, missing values of which estimated via Kalman, and comparisons are given. These processes are realized by programs written in MATLAB.