A recursive approach for estimating missing observations in an univariate time series

A linear recursive technique that does not use the Kalman filter approach is proposed to estimate missing observations in an univariate time series. It is assumed that the series follows an invertible ARIMA model. The procedure is based on the restricted forecasting approach, and the recursive linear estimators are optimal in terms of minimum mean-square error.     

Public–private versus public ownership and economic performance: evidence from Italian local utilities

In the last century local public services have often been reformed. The declared outcome of the most recent reforms is the privatization and liberalization of the sector. However, in almost all European countries, the privatization of local public services has been only partial, because local governments have sought to privatise a minority stake in the public owned-companies, while remaining committed to retaining public ownership and control over the longer-term as a means of protecting public interest. The phenomenon of mixed public–private companies emerged as a result of this process. In this context, the article investigates whether differences in financial performance can be found between public–private companies and totally public-owned enterprises. Empirical quantitative studies on this particular topic are quite lacking at the moment. The present study tries to fill this gap through an empirical analysis on a sample of 623 Italian local utilities. The results of the study suggest that there are differences in economic performance between local utility companies with varying degrees of public ownership. In particular, public–private utilities show better economic performance than publicly owned firms, especially in terms of profitability. The results also seem to suggest that the majority private ownership is not necessary for better performance. In other terms, public–private partnership—and not private majority ownership—seems to be the key point for good performance.     

An age‐structured model of population dynamics with dominant ages,delayed behavior,and oscillations

An age‐structured model of population dynamics with age‐dominance is proposed and analyzed. Existence and uniqueness of solutions are established as well as the uniqueness and local asymptotic stability of steady‐states. Conditions for convergence to or oscillation about the steady‐state are specified in some cases.     

Contextual Variability in Young Children's Gender Ingroup Stereotype

Self-categorization theorists (Oakes, Haslam & Turner, 1994) have shown that stereotypes are not rigid and fixed, and that they vary to reflect variations in the comparative context within which they are formed. In this paper we investigate stereotype variability in a sample of 6/7-year-old children. Participants describe a specific outgroup, and then describe the ingroup (that is 'boys' or 'girls', depending on the sex of the participant). There are two conditions: in condition one the outgroup is represented by adult 'men', if participants are boys, or by adult 'women', if participants are girls. In condition two the outgroup is represented by 'girls', if participants are boys, or by 'boys', if participants are girls. Results show that stereotypical traits attributed to the ingroup change significantly with changes in the frame of reference.     

On multivariate smoothed bootstrap consistency

This paper deals with the convergence in Mallows metric for classical multivariate kernel distribution function estimators. We prove the convergence in Mallows metric of a locally orientated kernel smooth estimator belonging to the class of sample smoothing estimators. The consistency follows for the smoothed bootstrap for regular functions of the marginal means. Two simple simulation studies show how the smoothed versions of the bootstrap give better results than the classical technique.     

An automatic classification and robust segmentation procedure of spatial objects

This paper proposes a statistical procedure for the automatic volumetric primitives classification and segmentation of 3D objects surveyed with high density laser scanning range measurements. The procedure is carried out in three main phases: first, a Taylor’s expansion nonparametric model is applied to study the differential local properties of the surface so to classify and identify homogeneous point clusters. Classification is based on the study of the surface Gaussian and mean curvature, computed for each point from estimated differential parameters of the Taylor’s formula extended to second order terms. The geometrical primitives are classified into the following basic types: elliptic, hyperbolic, parabolic and planar. The last phase corresponds to a parametric regression applied to perform a robust segmentation of the various primitives. A Simultaneous AutoRegressive model is applied to define the trend surface for each geometric feature, and a Forward Search procedure puts in evidence outliers or clusters of non stationary data. An erratum to this article can be found at