Confidence sets for asset correlations in portfolio credit risk

Asset correlations are of critical importance in quantifying portfolio credit risk and economic capitalin financial institutions. Estimation of asset correlation with rating transition data has focusedon the point estimation of the correlation without giving any consideration to the uncertaintyaround these point estimates. In this article we use Bayesian methods to estimate a dynamicfactor model for default risk using rating data (McNeil et al., 2005; McNeil and Wendin, 2007).Bayesian methods allow us to formally incorporate human judgement in the estimation of assetcorrelation, through the prior distribution and fully characterize a confidence set for the correlations.Results indicate: i) a two factor model rather than the one factor model, as proposed bythe Basel II framework, better represents the historical default data. ii) importance of unobservedfactors in this type of models is reinforced and point out that the levels of the implied asset correlationscritically depend on the latent state variable used to capture the dynamics of default,as well as other assumptions on the statistical model. iii) the posterior distributions of the assetcorrelations show that the Basel recommended bounds, for this parameter, undermine the levelof systemic risk.

Skill Rating by Bayesian Inference

Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure.

Model selection for the mixed logit with Bayesian estimation

In this paper, the mixed logit (ML) using Bayesian methods was employed to examine willingness-to-pay (WTP) to consume bread produced with reduced levels of pesticides so as to ameliorate environmental quality, from data generated by a choice experiment. Model comparison used the marginal likelihood, which is preferable for Bayesian model comparison and testing. Models containing constant and random parameters for a number of distributions were considered, along with models in ‘preference space’ and ‘WTP space’ as well as those allowing for misreporting. We found: strong support for the ML estimated in WTP space; little support for fixing the price coefficient a common practice advocated and adopted in the environmental economics literature; and, weak evidence for misreporting.

Bayesian design and conduct of phase II single-arm clinical trials with binary outcomes: a tutorial

The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.

Dating WF16: exploring the chronology of a Pre-Pottery Neolithic A settlement in the Southern Levant

A pre-requisite for understanding the transition to the Neolithic in the Levant is the establishment of a robust chronology, most notably for the late Epi-Palaeolithic and Pre-Pottery Neolithic A (PPNA) periods. In this contribution we undertake a dating analysis of the Pre-Pottery Neolithic site of WF16, southern Jordan, drawing on a sample of 46 AMS 14C dates. We utilise Bayesian methods to quantify an old wood effect to provide an offset that we factor into chronological models for a number of individual structures at WF16 and for the settlement as a whole. In doing so we address the influence of slope variations in the calibration curve and expose the significance of sediment and sample redeposition within sites of this nature. We conclude that for the excavated deposits at WF16 human activity is likely to have started by c. 11.84 ka cal bp and lasted for at least c. 1590 years, ceasing by c. 10.24 ka cal bp. This is marked by a particularly intensive period of activity lasting for c. 350 years centred on 11.25 ka cal bp followed by less intensive activity lasting a further c. 880 years. The study reveals the potential of WF16 as a laboratory to explore methodological issues concerning 14C dating of early Neolithic sites in arid, erosional environments.

Bayesian Estimation for Performance Measures of Two Diagnostic Tests in the Presence of Verification Bias

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Glucocorticoid: Major Factor for Reduced Immunogenicity of 2009 Influenza A (H1N1) Vaccine in Patients with Juvenile Autoimmune Rheumatic Disease

Objective. To assess the immunogenicity and safety of non-adjuvanted influenza A H1N1/2009 vaccine in patients with juvenile autoimmune rheumatic disease (ARD) and healthy controls, because data are limited to the adult rheumatologic population. Method's. A total of 237 patients with juvenile ARD [juvenile systemic lupus erythematosus (JSLE), juvenile idiopathic arthritis (JIA), juvenile dermatomyositis (JDM), juvenile scleroderma, and vasculitis] and 91 healthy controls were vaccinated. Serology for anti-H1N1 was performed by hemagglutination inhibition assay. Seroprotection rate, seroconversion rate, and factor-increase in geometric mean titer (GMT) were calculated. Adverse events were evaluated. Results. Age was comparable in patients and controls (14.8 +/- 3.0 vs 14.6 +/- 3.7 years, respectively; p = 0.47). Three weeks after immunization, seroprotection rate (81.4% vs 95.6%; p = 0.0007), seroconversion rate (74.3 vs 95.6%; p < 0.0001), and the factor-increase in GMT (12.9 vs 20.3; p = 0.012) were significantly lower in patients with juvenile ARD versus controls. Subgroup analysis revealed reduced seroconversion rates in JSLE (p < 0.0001), JIA (p = 0.008), JDM (p = 0.025), and vasculitis (p = 0.017). Seroprotection (p < 0.0001) and GMT (p < 0.0001) were decreased only in JSLE. Glucocorticoid use and lymphopenia were associated with lower seroconversion rates (60.4 vs 82.9%; p = 0.0001; and 55.6 vs 77.2%; p = 0.012). Multivariate logistic regression including diseases, lymphopenia, glucocorticoid, and immunosuppressants demonstrated that only glucocorticoid use (p = 0.012) remained significant. Conclusion. This is the largest study to demonstrate a reduced but adequate immune response to H1N1 vaccine in patients with juvenile ARD. It identified current glucocorticoid use as the major factor for decreased antibody production. The short-term safety results support its routine recommendation for patients with juvenile ARD. ClinicalTrials.gov; NCT01151644. (First Release Nov 15 2011; J Rheumatol 2012;39:167-73; doi:10.3899/jrheum.110721)

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Tecniche numeriche di studio della sicurezza stradale: il caso della provincia di Bologna

La tesi dottorale in oggetto prende spunto da alcune considerazioni di base relative alla salute di una comunità. Infatti quest’ultima si fonda sulla sicurezza dell’ambiente in cui vive e sulla qualità delle relazioni tra i suoi componenti. In questo ambito la mobilità rappresenta uno degli elementi di maggior criticità, sia per la sicurezza delle persone, che per la salute pubblica, che per le conseguenze sull’ambiente che ne derivano. Negli ultimi anni la circolazione stradale è notevolmente aumentata è questo ha portato a notevoli aspetti negativi, uno dei quali è connesso agli incidenti stradali. In tale ambito viene ricordato che l’Unione Europea ha da tempo indicato come obiettivo prioritario il miglioramento della sicurezza stradale e nel 2001 ha fissato il traguardo di dimezzare entro il 2010 il numero delle vittime degli incidenti stradali. Non ultima, l’approvazione da parte del Parlamento europeo e del Consiglio di un atto legislativo (d’imminente pubblicazione sulla GU Europea) relativo alla gestione della sicurezza in tutte le fasi della pianificazione, della progettazione e del funzionamento delle infrastrutture stradali, in cui si evidenzia l’esigenza di una quantificazione della sicurezza stradale. In tale contesto viene sottolineato come uno dei maggiori problemi nella gestione della sicurezza stradale sia la mancanza di un metodo affidabile per stimare e quantificare il livello di sicurezza di una strada esistente o in progetto. Partendo da questa considerazione la tesi si sviluppa mettendo in evidenza le grandezza fondamentali nel problema della sicurezza stradale, (grado di esposizione, rischio d’incidente e le possibili conseguenze sui passeggeri) e analizzando i sistemi adottati tradizionalmente per effettuare analisi di sicurezza: • Statistiche dei dati storici d’incidente; • Previsione da modelli basati su analisi di regressione dei dati incidentali; • Studi Before-After; • Valutazione da giudizi di esperti. Dopo aver analizzato gli aspetti positivi e negativi delle alternative in parola, viene proposto un nuovo approccio, che combina gli elementi di ognuno dei metodi sopra citati in un algoritmo di previsione incidentale. Tale nuovo algoritmo, denominato Interactive Highway Safety Design Model (IHSDM) è stato sviluppato dalla Federal Highway Administration in collaborazione con la Turner Fairbank Higway Research Center ed è specifico per le strade extraurbane a due corsie. Il passo successivo nello sviluppo della tesi è quello di un’analisi dettagliata del modello IHSDM che fornisce il numero totale di incidenti previsti in un certo intervallo temporale. Viene analizzata la struttura del modello, i limiti d’applicabilità, le equazioni che ne sono alla base e i coefficienti moltiplicativi relativi ad ogni caratteristica geometrica e funzionale. Inoltre viene presentata un’ampia analisi di sensibilità che permette di definire quale sia l’influenza d’ogni singolo Fattore di Previsione incidentale (Accident Predication Factor) sul risultato finale. Dai temi trattati, emerge chiaramente come la sicurezza è legata a più sistemi tra loro interconnessi e che per utilizzare e migliorare i modelli previsionali è necessario avere a disposizione dati completi, congruenti, aggiornati e facilmente consultabili. Infatti, anche quando sono disponibili elementi su tutti gli incidenti avvenuti, spesso mancano informazioni di dettaglio ma fondamentali, riguardanti la strada come ad esempio il grado di curvatura, la larghezza della carreggiata o l’aderenza della pavimentazione. In tale ottica, nella tesi viene presentato il Sistema Informativo Stradale (SIS) della Provincia di Bologna, concepito come strumento di gestione delle problematiche inerenti la viabilità e come strumento di supporto per la pianificazione degli interventi e la programmazione delle risorse da investire sulla rete. Viene illustrato come il sistema sia in grado di acquisire, elaborare ed associare dati georeferenziati relativi al territorio sia sotto forma di rappresentazioni grafiche, sia mediante informazioni descrittive di tipo anagrafico ed alfanumerico. Quindi viene descritto il rilievo ad alto rendimento, effettuato con l’ausilio di un laboratorio mobile multifunzionale (Mobile Mapping System), grazie al quale è stato possibile definire con precisione il grafo completo delle strade provinciali e il database contenente i dati relativi al patrimonio infrastrutturale. Tali dati, relativi alle caratteristiche plano-altimetriche dell’asse (rettifili, curve planimetriche, livellette, raccordi altimetrici, ecc...), alla sezione trasversale (numero e larghezza corsie, presenza di banchine, ecc..), all’ambiente circostante e alle strutture annesse vengono presentati in forma completa specificando per ognuno la variabilità specifica. Inoltre viene evidenziato come il database si completi con i dati d’incidentali georeferenziati sul grafo e compresivi di tutte le informazioni contenute nel modello ISTAT CTT/INC spiegandone le possibili conseguenze sul campo dell’analisi di sicurezza. La tesi si conclude con l’applicazione del modello IHSDM ad un caso reale, nello specifico la SP255 di S.Matteo Decima. Infatti tale infrastruttura sarà oggetto di un miglioramento strutturale, finanziato dalla Regione Emilia Romagna, che consistente nell’allargamento della sede stradale attraverso la realizzazione di una banchina pavimentata di 1.00m su entrambi i lati della strada dalla prog. km 19+000 al km 21+200. Attraverso l’utilizzo dell’algoritmo di previsione incidentale è stato possibile quantificare gli effetti di questo miglioramento sul livello di sicurezza dell’infrastruttura e verificare l’attendibilità del modello con e senza storia incidentale pregressa. Questa applicazione ad un caso reale mette in evidenza come le informazioni del SIS possano essere sfruttate a pieno per la realizzazione di un analisi di sicurezza attraverso l’algoritmo di previsione incidentale IHSDM sia nella fase di analisi di uno specifico tronco stradale che in quella fondamentale di calibrazione del modello ad una specifica rete stradale (quella della Provincia di Bologna). Inoltre viene sottolineato come la fruibilità e la completezza dei dati a disposizione, possano costituire la base per sviluppi di ricerca futuri, come ad esempio l’indagine sulle correlazioni esistenti tra le variabili indipendenti che agiscono sulla sicurezza stradale.

Mortality from road traffic accidents in Switzerland: longitudinal and spatial analyses

Road traffic accidents (RTA) are an important cause of premature death. We examined socio-demographic and geographical determinants of RTA mortality in Switzerland by linking 2000 census data to RTA mortality records 2000-2005 (ICD-10 codes V00-V99). Data from 5.5 million residents aged 18-94 years, 1744 study areas, and 1620 RTA deaths were analyzed, including 978 deaths (60.4%) in motor vehicle occupants, 254 (15.7%) in motorcyclists, 107 (6.6%) in cyclists, and 259 (16.0%) in pedestrians. Weibull survival models and Bayesian methods were used to calculate hazard ratios (HR), and standardized mortality ratios (SMR) across study areas. Adjusted HR comparing women with men ranged from 0.04 (95% CI 0.02-0.07) in motorcyclists to 0.43 (95% CI 0.32-0.56) in pedestrians. There was a u-shaped relationship with age in motor vehicle occupants and motorcyclists. In cyclists and pedestrians, mortality increased after age 55 years. Mortality was higher in individuals with primary education (HR 1.53; 95% CI 1.29-1.81), and higher in single (HR 1.24; 95% CI 1.05-1.46), widowed (HR 1.31; 95% CI 1.05-1.65) and divorced individuals (HR 1.62; 95% CI 1.33-1.97), compared to persons with tertiary education or married persons. The association with education was particularly strong for pedestrians (HR 1.87; 95% CI 1.20-2.91). RTA mortality increased with decreasing population density of study areas for motor vehicle occupants (test for trend p<0.0001) and motorcyclists (p=0.0021) but not for cyclists (p=0.39) or pedestrians (p=0.29). SMR standardized for socio-demographic and geographical variables ranged from 82 to 190. Prevention efforts should aim to reduce inequities across socio-demographic and educational groups, and across geographical areas, with interventions targeted at high-risk groups and areas, and different traffic users, including pedestrians.

Bayesian statistics in oncology: a guide for the clinical investigator

The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.

Public health risks of enterobacterial isolates producing extended-spectrum β-lactamases or AmpC β-lactamases in food and food-producing animals: an EU perspective of epidemiology, analytical methods, risk factors, and control options

The blaESBL and blaAmpC genes in Enterobacteriaceae are spread by plasmid-mediated integrons, insertion sequences, and transposons, some of which are homologous in bacteria from food animals, foods, and humans. These genes have been frequently identified in Escherichia coli and Salmonella from food animals, the most common being blaCTX-M-1, blaCTX-M-14, and blaCMY-2. Identification of risk factors for their occurrence in food animals is complex. In addition to generic antimicrobial use, cephalosporin usage is an important risk factor for selection and spread of these genes. Extensive international trade of animals is a further risk factor. There are no data on the effectiveness of individual control options in reducing public health risks. A highly effective option would be to stop or restrict cephalosporin usage in food animals. Decreasing total antimicrobial use is also of high priority. Implementation of measures to limit strain dissemination (increasing farm biosecurity, controls in animal trade, and other general postharvest controls) are also important.