DISCREPANCY FUNCTION

A 'discrepancy function' is a mathematical function which describes how closely a structural model conforms to observed data. Larger values of the discrepancy function indicate a poor fit of the model to data. In general, the parameter estimates for a given model are chosen so as to make the discrepancy function for that model as small as possible.^[1]
There are several basic types of discrepancy functions, including Maximum Likelihood (ML), Generalized Least Squares (GLS), and Uweighted Least Squares (ULS), which are considered the "classical" discrepancy functions.^[2] Discrepancy functions all meet the following basic criteria:

★ They are non-negative, i.e., always greater than or equal to zero.

★ They are zero only if the fit is perfect, i.e., if the model and parameter estimates perfectly reproduce the observed data.

★ The discrepancy function is a continuous function of the elements of 'S', the sample covariance matrix, and 'Σ(θ)', the "reproduced" estimate of 'S' obtained by using the parameter estimates and the structural model.^[3]

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References

See also

References

1. Department of Chemistry and Biochemistry at Florida State University
2. Discrepancy Functions Used in SEM
3. anything

See also

★ Low-discrepancy sequence

★ Constructions of low-discrepancy sequences