Nonlinear mixed-effects models with scale mixture of skew-normal distributions

Aiming to avoid the sensitivity in the parameters estimation due to atypical observations or skewness, we develop asymmetric nonlinear regression models with mixed-effects, which provide alternatives to the use of normal distribution and other symmetric distributions. Nonlinear models with mixed-effects are explored in several areas of knowledge, especially when data are correlated, such as longitudinal data, repeated measures and multilevel data, in particular, for their flexibility in dealing with measures of areas such as economics and pharmacokinetics. The random components of the present model are assumed to follow distributions that belong to scale mixtures of skew-normal (SMSN) distribution family, that encompasses distributions with light and heavy tails, such as skew-normal, skew-Student-t, skew-contaminated normal and skew-slash, as well as symmetrical versions of these distributions. For the parameters estimation we obtain a numerical solution via the EM algorithm and its extensions, and the Newton-Raphson algorithm. An application with pharmacokinetic data shows the superiority of the proposed models, for which the skew-contaminated normal distribution has shown to be the most adequate distribution. A brief simulation study points to good properties of the parameter vector estimators obtained by the maximum likelihood method.     

Detecting serial dependencies with the reproducibility probability autodependogram

The autodependogram is a graphical device recently proposed in the literature to analyze autodependencies. This paper proposes a normalization of this diagram taking into consideration the concept of reproducibility probability (RP). The result is a novel tool, named RP-autodependogram, which permits to study the strength and the stability of the evidence about the presence of lag-dependence. A simulation study on well-established time-series models is carried out to investigate the behavior of the RP-autodependogram also in comparison with other diagrams studying autodependencies. An application to financial data is finally considered to appreciate its usefulness in the identification of parametric/nonparametric models.     

Local Labour Markets Identification: a United Point of View*

Abstract. In this paper a unifying point of view is discussed of the methodologies and algorithms actually proposed for the identification of local labour market. The emphasis is on the main contributions to two broad methodological areas of functional regionalization and homogeneous spatial clustering. Most Italian contributions are considered in detail and some critical notes are pointed out in order to show that the methodological background is just the same for both approaches. The interpretations of the outputs of the proposed methodologies is discussed in terms of local labour markets.