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This paper introduces a class of scale mixtures of normal selection factor (SMNSF) analysis models which are robust against departures from normality and designed to correct sample-selection bias.
The He and Ne in all of the MMs studied are mixtures of normal SW and IFSW.
The derivation is based on successive approximations of mixtures of normal distributions which, in turn, provide approximations of the conditional distributions of the signal given the observations.
The common method to treat polar/nonpolar systems, and also mixtures of normal compounds where the sizes are significantly different is to use binary interaction parameters.
In this study, we locate their change-points by formulating the HP filter as a generalized unobserved components model with error terms of mixtures of normal distributions.
This paper proposes an extension of nonlinear regression models using the skew-scale mixtures of normal (SSMN) distributions proposed by Ferreira et al.
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Wada and Perron (2006) utilize up to two disturbances with mixtures of normals.
The previous analysis is complemented with estimations that combine two mixtures of normals.
We then continue with models that contemplate combinations of two mixtures of normals, and explore the possibility of a model that admits up to three mixtures of normals, in order to find the best specification for the Peruvian economy.
In consequence, there are 2 m possible states, where m is the number of disturbances with mixtures of normals.
The distributions of the innovations in both noise and signal are modeled robustly using mixtures of normals.
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