The performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values. Contrary to the well known procedures, the new optimization algorithm is based on statistical methods since it uses a combination of Linear Mixed Effect Models and Response Surface Methodology techniques. In particular, the Method of Steepest Ascent which is well known for the case of an Ordinary Least Squares setting and a linear response surface has been generalized to be applicable for repeated measurements situations and for response surfaces of order o <= 2.
Miilticollinesrity among the columns of regressor variables is known to cause severe distortion of the least squares estimates of the parameters in a multiple linear regression ...
An attractive alternative to least‐squares data modeling techniques is the use of absolute value error criteria. Unlike the least‐squares techniques the inclusion of some infini...
Very general multilinear models, called CANDELINC, and a practical least-squares fitting procedure, also called CANDELINC, are described for data consisting of a many-way array....
Abstract In this paper a general algorithm is provided for maximum likelihood fitting of deterministic models subject to Gaussian‐distributed residual variation (including any t...
From the Publisher: Using a practical approach, it discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniq...
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