Abstract
Asymptotic normality of modified LS estimator for mixture of nonlinear regressions
Highlights
1 Introduction Nonlinear regression models are widely used in analysis of statistical data [14, 16]
If the concentrations of components in the mixture are different for different observations the model of mixture with varying concentrations (MVC) can be applied [1, 12, 11]
In this paper we consider regression technique application to data, which are described by the model of mixture with varying concentrations
Summary
Nonlinear regression models are widely used in analysis of statistical data [14, 16]. In many applications the observed data are derived from a mixture of components with different dependencies between the variables in different components. In this case a finite mixture model can be used to describe the data [15, 19, 2]. The consistency of mLS estimators in regression MVC models was demonstrated in [9]. Our aim is to derive conditions of mLS estimators asymptotic normality and construct confidence sets for the true values of parameters.
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