Assessing fat-tailed sequential forecast distributions for the Dow-Jones index with logarithmic scoring rules

We use the logarithmic scoring rule for distributions to assess a variety of fat-tailed sequential forecasting distributions for the Dow-Jones industrial stock index from 1980 to the present. The methodology applies Bruno de Finetti''s contributions to understanding how to compare the quality of different coherent forecasting distributions for the same sequence of observations, using proper scoring rules. Four different forms of forecasting distributions are compared: a mixture Normal, a mixture of convex combinations of three Normal distributions, a mixture exponential power distribution, and a mixture of a convex combination of three exponential power distributions. The mixture linear com…

EFFETTI DELLA TERAPIA MULTISISTEMICA IN ACQUA SULLE ABILITA’ GROSSOMOTORIE E COGNITIVE IN SOGGETTI CON DISTURBO DELLO SPETTRO AUTISTICO

Lo scopo del presente studio è stato quello di studiare gli effetti della “terapia multisistemica in acqua” (TMA) sulle abilità grossomotorie e cognitive in bambini con disturbo dello spettro autistico. Le attività hanno coinvolto sei soggetti e si sono svolte attraverso la “pianificazione” di un intervento individualizzato e interpersonale volto a ridurre i sintomi e migliorare le capacità comunicative e relazionali del bambino avvalendosi degli ambienti strutturati delle piscine pubbliche. Dopo l’intervento terapeutico, i soggetti hanno mostrato un miglioramento del quoziente di sviluppo grosso-motorio e una modificazione degli schemi cognitivi, comportamentali, comunicativi ed emotivi. I…

Filling in long gap sequences by performing jointly EOF and FDA

In this paper the EOF methodology is performed jointly with the FDA approach on a spatiotemporal multivariate data set with the aim to fill in missing values as accurately as possible when long gap sequences occur. Simulated data sets, containing ”artificial” gaps, are considered in order to test the performance of two proposed procedures; in the first one, observed data are reconstructed by EOF and then converted into functional ones; in the second one, observed data are transformed into functional ones and then EOF reconstruction is applied. By comparing some performance indicators computed for the two procedures, it is shown that a pre-processing of data by FDA, followed by the EOF, may …

Riconoscimento dei legni visitati da Reticulitermes lucifugus Rossi da parte di individui della stessa specie.

From a multivariate spatio-temporal array to a multipollutant - multisite Air Quality Index

AQIs are computed on air pollution data that are usually collected according to time, space and type of pollutant: in a given town/region, data consisting of hourly levels of K pollutants recorded in S monitoring sites, are usually organized in a three-mode array. A first aggregation step usually concerns time, and allows to pass from hourly data to a daily synthesis: in this paper data will be aggregated by time according to the guidelines provided by the national agencies producing the three mode array X. Here we will propose a new approach to get a Multipollutant-Multisite Air Quality Index time series from a multivariate spatio-temporal array. This implies a two step aggregation, accord…

Air quality assessment via functional principal component analysis

The knowledge of the global urban air quality situation represents the first step to face air pollution issues. For the last decades many urban areas can rely on a monitoring network, recording hourly data for the main pollutants. Such data need to be aggregated according to different dimensions, such as time, space and type of pollutant, in order to provide a synthetic air quality index which takes into account interactions among pollutants and correlation among monitoring sites.This paper focuses on Functional Principal Component techniques for the statistical analysis of a set of environmental data x(spt), where s stands for the monitoring site, p for the pollutant and t for time, usuall…

Bayesian Inference for the Exponential Power Function Parameters

This paper addresses the problem of obtaining the marginal posterior distributions, via Gibbs Sampler, for the parameters of the well-known generalized error distribution called Exponential Power Function (E.P.F.). This density represents a family of unimodal symmetric distributions with shapes varying from leptokurtic to platikurtic.

Riconoscimento di legni visitati da Reticulitermes lucifugus Rossi(Isoptera: Rinotermitidae) da parte di individui della stessa specie

Il modulo elastico statico e dinamico del calcestruzzo

Principal components for multivariate spatiotemporal functional data

Multivariate spatio-temporal data consist of a three way array with two dimensions’ domains both structured, temporally and spatially; think for example to a set of different pollutant levels recorded for a month/year at different sites. In this kind of dataset we can recognize time series along one dimension, spatial series along another and multivariate data along the third dimension. Statistical techniques aiming at handling huge amounts of information are very important in this context and classical dimension reduction techniques, such as Principal Components, are relevant, allowing to compress the information without much loss. Although time series, as well as spatial series, are recor…

On VaR using modified gaussian copula

The problem of modeling asset returns is one of the most important issue in finance. People generally use Gaussian processes because of their tractable properties for computation. However, it is well known that asset returns are fat-tailed leading to an underestimation of the risk. One of the most recent proposals is to model the interdependence of asset returns, for example in a portfolio, by means of Copulas and choose marginal distributions with fat tail to fit the single asset returns. The aim of the paper is to show first results concerning the evaluation of Portfolio Value-at-Risk (VaR) using the Gaussian copula, modified by introducing a particular correlation coefficient, and assumi…

EOFs for gap filling in multivariate air quality data: a FDA approach

Missing values are a common concern in spatiotemporal data sets. During recent years a great number of methods have been developed for gap filling. One of the emerging approaches is based on the Empirical Orthogonal Function (EOF) methodology, applied mainly on raw and univariate data sets presenting irregular missing patterns. In this paper EOF is carried out on a multivariate space-time data set, related to concentrations of pollutants recorded at different sites, after denoising raw data by FDA approach. Some performance indicators are computed on simulated incomplete data sets with also long gaps in order to show that the EOF reconstruction appears to be an improved procedure especially…