Bayesian outlier detection in Capital Asset Pricing Model

We propose a novel Bayesian optimisation procedure for outlier detection in the Capital Asset Pricing Model. We use a parametric product partition model to robustly estimate the systematic risk of an asset. We assume that the returns follow independent normal distributions and we impose a partition structure on the parameters of interest. The partition structure imposed on the parameters induces a corresponding clustering of the returns. We identify via an optimisation procedure the partition that best separates standard observations from the atypical ones. The methodology is illustrated with reference to a real data set, for which we also provide a microeconomic interpretation of the detected outliers

Bayesian Analysis of Marginal Log-Linear Graphical Models for Three Way Contingency Tables

This paper deals with the Bayesian analysis of graphical models of marginal independence for three way contingency tables. We use a marginal log-linear parametrization, under which the model is defined through suitable zero-constraints on the interaction parameters calculated within marginal distributions. We undertake a comprehensive Bayesian analysis of these models, involving suitable choices of prior distributions, estimation, model determination, as well as the allied computational issues. The methodology is illustrated with reference to two real data sets.Comment: 34 pages, 7 tables, 3 figure

Probability Based Independence Sampler for Bayesian Quantitative Learning in Graphical Log-Linear Marginal Models

Bayesian methods for graphical log-linear marginal models have not been developed in the same extent as traditional frequentist approaches. In this work, we introduce a novel Bayesian approach for quantitative learning for such models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. Furthermore, the likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and MCMC methods are needed. Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for graphical log-linear marginal models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation. By this strategy, we achieve to move within the desired target space. At each step, we directly work with well-defined probability distributions. Moreover, we can exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated by a simulation study and a real dataset

Effect of slaughter age on meat qualitative traits of veals calves

Scopo del presente lavoro è stato quello di verificare alcune caratteristiche organolettiche e chimico bromatologhe della carne di 15 vitelli di razza Frisona. I vitelli sono stati macellati a 140, 160 e 190 giorni. I parametri analizzati non sono stati significativamente influenzati dall'età di macellazione se non per una riduzione del tenore proteico della sostanza secca a 160 giorni. Pertanto la scelta dell'età di macellazione, per il periodo studiato, può essere fatta sulla base di criteri di natura economica piuttosto che riferita ad aspetti qualitativi della carn

Default Probability Estimation via Pair Copula Constructions

In this paper we present a novel Bayesian approach for default probability estimation. The methodology is based on multivariate contingent claim analysis and pair copula theory. Balance sheet data are used to asses the rm value and to compute its default probability. The rm pricing function is obtained via a pair copula approach, and Monte Carlo simulations are used to calculate the default probability distribution. The methodology is illustrated through an application to defaulted rms data

Default probability estimation via pair copula constructions

publisher: Elsevier articletitle: Default probability estimation via pair copula constructions journaltitle: European Journal of Operational Research articlelink: http://dx.doi.org/10.1016/j.ejor.2015.08.026 content_type: article copyright: Copyright © 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved

Osservatorio territoriale droga e tossicodipendenze. Il fenomeno delle dipendenze nel territorio della ASL della Provincia di Sondrio. Rapporto 2008.

Report on the state of legal and illegal substances use in the territory of Sondrio province.Il report analizza il fenomeno delle dipendenze nel territorio della Provincia di Sondrio. La descrizione del fenomeno si sviluppa intorno all\u27analisi degli indicatori individuati dall\u27Osservatorio Europeo delle Dipendenze di Lisbona (OEDT): 1-uso di sostanze nella popolazione generale (questo indicatore va a rilevare i comportamenti nei confronti di alcol e sostanze psicoattive da parte della popolazione generale); 2-prevalenza d\u27uso problematico delle sostanze psicoattive; 3-domanda di trattamento degli utilizzatori di sostanze; 4-mortalit? degli utilizzatori di sostanze; 5-malattie infettive. Altri due importanti indicatori che si stanno sviluppando, e che vengono qui illustrati, sono l\u27analisi delle Schede di Dimissione Ospedaliera (SDO) e gli indicatori relativi alle conseguenza sociali dell\u27uso di droghe (criminalit? droga correlata). Inoltre sono state applicate diverse metodologie standard di stima sia per quantificare la quota parte sconosciuta di utilizzatori di sostanze che non afferiscono ai servizi, sia per identificarne alcune caratteristiche

The Disturbed Habitat and Its Effects on the Animal Population

Changes in the “habitat” may interfere with the normal functioning of all biological systems. The existence of relationships between environmental changes and health in humans and animal species is well known and it has become generally accepted that poor health affects the animal’s natural behaviors and animal welfare and, consequently, food safety and animal production quality. Microclimate alterations, husbandry-management conditions, quality of human-animal interactions, feeding systems, and rearing environment represent the main factors that could negatively affect animal welfare and may produce behavioral, biochemical, endocrine, and pathological modifications in domestic and wild animals. Particularly, high stress levels can reduce the immune system response and promote infectious diseases. Adverse socio-environmental factors can represent a major stimulus to the development of different pathologies. This chapter will discuss the main pathological modifications described in domestic and wild animals due to “disturbed habitat” paying more attention to critical points detected in standard breeding systems