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- W2162046864 abstract "Summary An important problem in applied statistics is fitting a given model function f(β) with unknown parameters β to a data vector y. Minimizing the residual sum of squares provides the least squares estimates of β. If f(β) is linear in β the precision of these estimates is well-known. In a nonlinear case approximate (though asymptotically exact) confidence statements can be made. Beale [1] introduced measures of nonlinearity which can be used to indicate when approximate confidence statements are appropriate. Guttman and Meeter [2] showed that in some, severely nonlinear, cases Beale's measures do not give the right indication. In this paper two new nonlinearity measures are introduced and their use is illustrated on a practical problem described by Witt [3]. A more detailed discussion of the theoretical background can be found in references [1] and [2]." @default.
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- W2162046864 title "Nonlinearity measures: a case study" @default.
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- W2162046864 doi "https://doi.org/10.1111/j.1467-9574.1975.tb00253.x" @default.
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