Francesco Bartolucci

Bio: Francesco Bartolucci is an academic researcher from University of Perugia. The author has contributed to research in topics: Latent class model & Expectation–maximization algorithm. The author has an hindex of 31, co-authored 214 publications receiving 2629 citations. Previous affiliations of Francesco Bartolucci include University of Urbino.

Validity of the 36-item Persian (Farsi) version of the World Health Organization Disability Assessment Schedule (WHODAS) 2.0

Abstract: The purpose of this study was to validate a self-administered 36-item Persian (Farsi) version of the World Health Organization (WHO) Disability Assessment Schedule II (now referred to as WHODAS 2.0) for assessment of psychiatric patients’ perceptions of their functioning and disability. WHODAS 2.0 items were analyzed using two approaches. Reliability, consistency, and factor structure were assessed using Cronbach’s α and factor analysis, and item response theory (IRT) was used to determine how well the WHODAS 2.0 items fitted the Rasch paradigm. Data were collected from 614 psychiatric outpatients in Tehran. The mean overall disability score for the sample was 37.57. The scale had excellent reliability (Cronbach’s α = 0.94). The IRT-based analysis showed that overall the set of items had a poor fit to the Rasch paradigm; the exceptions were items belonging to domains D1 (cognition), D2 (mobility), and D5 (life activities). There were several problematic items associated with dimensions D3 (self-ca...

Causal inference in paired two-arm experimental studies under noncompliance with application to prognosis of myocardial infarction

Abstract: Motivated by a study about prompt coronary angiography in myocardial infarction, we propose a method to estimate the causal effect of a treatment in two-arm experimental studies with possible noncompliance in both treatment and control arms. We base the method on a causal model for repeated binary outcomes (before and after the treatment), which includes individual covariates and latent variables for the unobserved heterogeneity between subjects. Moreover, given the type of noncompliance, the model assumes the existence of three subpopulations of subjects: compliers, never-takers, and always-takers. We estimate the model using a two-step estimator: at the first step, we estimate the probability that a subject belongs to one of the three subpopulations on the basis of the available covariates; at the second step, we estimate the causal effects through a conditional logistic method, the implementation of which depends on the results from the first step. The estimator is approximately consistent and, under certain circumstances, exactly consistent. We provide evidence that the bias is negligible in relevant situations. We compute standard errors on the basis of a sandwich formula. The application shows that prompt coronary angiography in patients with myocardial infarction may significantly decrease the risk of other events within the next 2 years, with a log-odds of about − 2. Given that noncompliance is significant for patients being given the treatment because of high-risk conditions, classical estimators fail to detect, or at least underestimate, this effect. Copyright © 2013 John Wiley & Sons, Ltd.

Determinants of Job Satisfaction in Young Russian Workers

Abstract: A growing economic literature regards the analysis of job satisfaction; however, as for young people the investigations are still scarce. I n this paper we analyse job satisfaction among Russian young workers by using the data collected f or four items, the first of which concerns the general satisfaction about the job; the other three items concern specific aspects of job satisfaction with respect to work condition, earning, and opport unity for professional growth. The corresponding response variables are categorical wi th five ordered categories, from “absolutely unsatisfied” to “absolutely satisfied”. The longitu dinal dataset also contains personal information about the respondents (gender, age, marital status, number of children, educational level, etc.). We estimate ordered logit models of job satisfaction w ith individual fixed effects for a panel data of Russian young workers, carrying out separate analys es for the general job satisfaction variable and three variables on specific aspects of job satisfac tion. If wages adjusted to fully compensate workplace disamenities, we would expect that differ ences in job satisfaction across individuals would not be systematically related to wage differe ntials, ceteris paribus. But this is not the case f or our panel: for all but one of the samples considere d there is at least one job satisfaction variable with a significantly positive wage effect. We, ther efore, interpret this result as a failure of the th eory of compensating wage differentials in the Russian y outh labour market. There is the interesting exception, though, that compensating wage different ials do seem at work among the older subjects in the panel. Our estimates also show strong gender and location effects.

Testing for State Dependence in Binary Panel Data with Individual Covariates by a Modified Quadratic Exponential Model

Abstract: We propose a test for state dependence in binary panel data with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a Conditional Maximum Likelihood (CML) approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. Therefore, it is possible to implement a t-test for this hypothesis which may be very simply performed and attains the nominal significance level under several structures of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state ...

A multilevel latent Markov model for the evaluation of nursing homes' performance.

Abstract: The periodic evaluation of health care services is a primary concern for many institutions. In this work, we focus on nursing home services with the aim to produce a ranking of a set of nursing homes based on their capability to improve - or at least to keep unchanged - the health status of the patients they host. As the overall health status is not directly observable, latent variable models represent a suitable approach. Moreover, given the longitudinal and multilevel structure of the available data, we rely on a multilevel latent Markov model where patients and nursing homes are the first and the second level units, respectively. The model includes individual covariates to account for the patient case-mix and the impact of nursing home membership is modeled through a pair of correlated random effects affecting the initial distribution and the transition probabilities between different levels of health status. Through the prediction of these random effects we obtain a ranking of the nursing homes. Furthermore, the proposed model is designed to address non-ignorable dropout, which typically occurs in these contexts because some elderly patients die before completing the survey. We apply our model to the Long Term Care Facilities dataset, a longitudinal dataset gathered from Regione Umbria (Italy). Our results are robust to the sensitivity parameter involved (the number of latent states) and show that differences in nursing homes' performances are statistically significant. The authors certify that they have the right to deposit this contribution in its published format with MPRA.

Estimation and Inference in Econometrics

Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Abstract: Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

Handbook of Markov Chain Monte Carlo

Abstract: Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng Spatial point processes, Mark Huber The data augmentation algorithm: Theory and methodology, James P. Hobert Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox Statistical ecology, Ruth King Gaussian random field models for spatial data, Murali Haran Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger MCMC for state space models, Paul Fearnhead MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens Applications of MCMC in fisheries science, Russell B. Millar Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Western

Education at a Glance

Abstract: A detailed review of the education sector in Australia as in the data provided by the 2006 edition of the OECD's annual publication, 'Education at a Glance' is presented. While the data has shown that in almost all OECD countries educational attainment levels are on the rise, with countries showing impressive gains in university qualifications, it also reveals that a large of share of young people still do not complete secondary school, which remains a baseline for successful entry into the labour market.