Penalized likelihood inference in extreme value analyses

Models for extreme values are usually based on detailed asymptotic argument, for which strong ergodic assumptions such as stationarity, or prescribed perturbations from stationarity, are required. In most applications of extreme value modelling such assumptions are not satisfied, but the type of departure from stationarity is either unknown or complex, making asymptotic calculations unfeasible. This has led to various approaches in which standard extreme value models are used as building blocks for conditional or local behaviour of processes, with more general statistical techniques being used at the modelling stage to handle the non-stationarity. This paper presents another approach in this direction based on penalized likelihood. There are some advantages to this particular approach: the method has a simple interpretation; computations for estimation are relatively straightforward using standard algorithms; and a simple reinterpretation of the model enables broader inferences, such as confidence intervals, to be obtained using MCMC methodology. Methodological details together with applications to both athletics and environmental data are given.     

EXTERNAL AIDS FOR SOCIAL MEMORY

Collective or social memories are not just a way for accumulating and preserving but also for sharing and developing knowledge. Indeed, as knowledge is made explicit and elaborated by a community, it enriches the local culture and the current practices, becoming a basis for communication and learning. This paper addresses the concept of 'social memory' in a specific 'community of practice': teachers and students of primary schools. The work is developed within HIPS1 (Hyper Interaction within Physical Space), a three-year (1997-2000) research project funded by the European Commission within the I 3 (I-Cube) Programme. HIPS is a hand-held location-aware tourist guide that delivers information related to the surrounding space mainly reacting to the physical movements of visitors (Benelli et al . 1999). The guide is designed to minimize the boundary between the physical space and the related information through a number of situated and contextual-aware interaction mechanisms. In the paper we present a specific application of HIPS as tool to support the creation of a social memory. First, we illustrate the theoretical framework, the cultural psychology (Vygotsky 1978), which we adopted to design the tool as an external aid for social memory. Afterwards we describe the user study and the design process that resulted in the development of an early prototype. The conclusions are a reflection about the use of new technology to open new learning opportunities for students.     

Parsimonious periodic autoregressive models for time series with evolving trend and seasonality

Statistics and Computing - This paper proposes an extension of Periodic AutoRegressive (PAR) modelling for time series with evolving features. The large scale of modern datasets, in fact, implies...     

A Well-Being Indicator for the Italian Provinces

Social Indicators Research - In recent years, a significant number of papers has been published providing alternative measures of progress and well-being to Gross Domestic Product. Most of these...     

A note on the decidability of de Finetti's coherence

To check the de Finetti coherence of a putative probability assigned to a classA of events, we must know the possible combinations of truth values (constituents) of any finite class of events inA. Even for a very simple, finite,A this can be impossible. In this case the notion of DF coherence cannot be applied to some or all the putative probabilities on this class of events.     

Decomposition by subpopulations of the Zenga-84 inequality curve and the related index $$\zeta $$: an application to 2014 Bank of Italy survey

Statistical Methods & Applications - This paper describes an innovative procedure to decompose by subpopulations the values assumed by the Zenga-84 inequality curve Z(p). This decomposition...     

Bayesian estimation of the latent dimension and communities in stochastic blockmodels

Statistics and Computing - Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with...     

Exploring unobserved heterogeneity in perinatal and neonatal mortality risks: The case of an Italian sharecropping community, 1900–39

Previous researchers have found that traditional determinants explain only a limited part of the variation in perinatal and infant mortality at the family level. In the study reported in this paper, we explored the factors that make the perinatal/neonatal death risk more heterogeneous across families. We estimated logistic regressions with cluster random effects at the maternal level, using data from the Italian village of Granarolo from 1900 to 1939. We estimated the effects of selected predictors on perinatal/neonatal mortality and unexplained inter-family variation. We found that non-rural skilled and lower-skilled workers experienced higher perinatal and neonatal mortality risks. Unexplained heterogeneity at the maternal level was lower for women living in sharecropper families than for those in landless labourer and non-rural worker families. Unexplained perinatal and neonatal mortality components were also due to socio-economic differences and were not necessarily related only to maternal biological features or shared genetic frailty.     

Robust inference for parsimonious model-based clustering

We introduce a robust clustering procedure for parsimonious model-based clustering. The classical mclust framework is robustified through impartial trimming and eigenvalue-ratio constraints (the tclust framework, which is robust but not affine invariant). An advantage of our resulting mtclust approach is that eigenvalue-ratio constraints are not needed for certain model formulations, leading to affine invariant robust parsimonious clustering. We illustrate the approach via simulations and a benchmark real data example. R code for the proposed method is available at https://github.com/afarcome/mtclust.