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On the ls-norm Generalization of the NLS Method for the Bass Model

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Abstract (Original Language): 
The best-known and widely used model in diffusion research is the Bass model. Estimation of its parameters has been approached in the literature by various methods, among which a very popular one is the nonlinear least squares (NLS) method proposed by Srinivasan and Mason. In this paper, we consider the ls-norm (1  s <1) generalization of the NLS method for the Bass model. Our focus is on the existence of the corresponding best ls-norm estimate. We show that it is possible for the best ls-norm estimate not to exist. As a main result, two theorems on the existence of the best ls-norm estimate are obtained. One of them gives necessary and sufficient conditions which guarantee the existence of the best ls-norm estimate.
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