Abstract
A general jackknife estimator for the asymptotic covariance of moment estimators is considered in the case when the sample is taken from a mixture with varying concentrations of components. Consistency of the estimator is demonstrated. A fast algorithm for its calculation is described. The estimator is applied to construction of confidence sets for regression parameters in the linear regression with errors in variables. An application to sociological data analysis is considered.
Highlights
Finite Mixture Models (FMM) are widely used in the analysis of biological, economic and sociological data
For a comprehensive survey of different statistical techniques based on FMMs, see [9]
ACM) in the case when the data are described by the Mixtures with Varying Concentrations (MVC) model
Summary
Finite Mixture Models (FMM) are widely used in the analysis of biological, economic and sociological data. In this paper we consider application of the jackknife technique to the estimation of asymptotic covariance matrix ACM) in the case when the data are described by the MVC model. We obtained a general theorem on consistency of the jackknife estimators for ACM for moment estimators in the MVC models and apply this result to construct confidence sets for regression coefficients in linear errors-in-variables models for MVC data.
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