Determining Dependency Structures and Estimating Nonlinear Regression Errors Without Doing Regression 1995


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    • Author:  by Carsten Peterson  

    • Abstract:  : A general method is discussed, the ffi -test, which establishes functional dependencies given a table of measurements. The approach is based on calculating conditional probabilities from data densities. Imposing the requirement of continuity of the underlying function the obtained values of the conditional probabilities carry information on the variable dependencies. The power of the method is illustrated on synthetic timeseries with different time-lag dependencies and noise levels. For N data points the computational demand is N 2 . Also, the same method is used for estimating nonlinear regression errors and their distributions without performing regression. Comparing the predicted residual errors with those from linear models provides a signal for nonlinearity. The virtue of the method in the context of feedforward neural networks is stressed with respect to preprocessing data and tracking residual errors. 1 carsten@thep.lu.se Motivation Successful regression of a system given...

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