Distribution-Free Methods of Approximating Nonlinear Marketing Relationships

1982 ◽  
Vol 19 (3) ◽  
pp. 372 ◽  
Author(s):  
Roland T. Rust ◽  
Elizabeth O. Bornman
Keyword(s):  
Distribution Free ◽  
Marketing Relationships

Distribution-Free Methods of Approximating Nonlinear Marketing Relationships

1982 ◽  
Vol 19 (3) ◽  
pp. 372-374 ◽  
Author(s):  
Roland T. Rust ◽  
Elizabeth O. Bornman
Keyword(s):  
Curve Fitting ◽  
Nonlinear Function ◽  
Model Specification ◽  
Trial And Error ◽  
Explicit Model ◽  
Distribution Free ◽  
Nonlinear Fitting ◽  
Functional Relationships ◽  
Marketing Relationships ◽  
Implicit Model

Many, if not most, of the important functional relationships in marketing are inherently nonlinear. Fitting a nonlinear function to data involves first the specification of a model, and then the estimation of the model's parameters. Where theory gives insufficient guidance for the specification of a model, specification based on observation of the scatterplot may be required. In many instances it is difficult to specify a model accurately solely on the basis of scatterplot observation. In those instances the researcher's power of observation can be enhanced by use of a distribution-free method of approximating the underlying function. The authors discuss alternative distribution-free methods, and demonstrate how the implicit model specification process of exhaustive trial and error curve-fitting can be replaced by an explicit model specification process based on observing an approximation of the underlying function.

Adaptive Distribution-Free Tests

1973 ◽  
Vol 2 (4) ◽  
pp. 337-356
Author(s):  
Ronald Randles ◽  
Robert Hogg
Keyword(s):  
Distribution Free

Statistics Distribution-Free

1957 ◽  
Vol 2 (4) ◽  
pp. 102-103
Author(s):  
ALLEN L. EDWARDS
Keyword(s):  
Distribution Free
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