Showing papers by "Francesco Bartolucci published in 2013"
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TL;DR: In this paper, a modified version of the three-step latent class approach is used to estimate a latent Markov model with individual covariates and possible dropout, which represents an useful estimation tool when a large number of observed variables and covariates occurs in the model.
Abstract: We illustrate the use of a modified version of the three-step latent class approach in order to estimate a latent Markov model with individual covariates and possible dropout. This approach represents an useful estimation tool when a large number of observed variables and covariates occurs in the model. Motivated by a study on the health status of elderly people hosted in Italian nursing homes, we address the problem to deal with informative missing responses and dropout due to the death of the patient. The proposed model allows us to account for both these types of missingness. We also consider a model in which time-constant and time varying covariates affect the initial and transition probabilities of the latent process, through a suitable parametrization. Aim of the study is to estimate the effect of each nursing home on the probability of transition between latent states, corresponding to different levels of the health status of the patients, and on the probability of dropout.
15 citations
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TL;DR: In this paper, a test for state dependence in binary panel data under the dynamic logit model with individual covariates is proposed, where the level of association between the response variables is measured by a single parameter that may be estimated by a conditional maximum likelihood approach.
Abstract: We propose a test for state dependence in binary panel data under the dynamic logit model 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 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. This allows us to implement a Wald test for this hypothesis which may be very simply performed and attains the nominal significance level under any structure 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 dependence. The test is illustrated by an application based on data coming from the Panel Study of Income Dynamics.
8 citations
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TL;DR: In this paper, an event history (EH) extension of the LM approach is proposed to avoid bias when using a model of this type in the presence of informative drop-out.
Abstract: Latent Markov (LM) models represent an important tool of analysis of longitudinal data when response variables are affected by time-varying unobserved heterogeneity, which is accounted for by a hidden Markov chain. In order to avoid bias when using a model of this type in the presence of informative drop-out, we propose an event-history (EH) extension of the LM approach that may be used with multivariate longitudinal data, in which one or more outcomes of a different nature are observed at each time occasion. The EH component of the resulting model is referred to the interval-censored drop-out, and bias in LM modeling is avoided by correlated random effects, included in the different model components, which follow a common Markov chain. In order to perform maximum likelihood estimation of the proposed model by the Expectation-Maximization algorithm, we extend the usual backward-forward recursions of Baum and Welch. The algorithm has the same complexity of the one adopted in cases of non-informative drop-out. Standard errors for the parameter estimates are derived by using the Oakes' identity. We illustrate the proposed approach through an application based on data coming from a medical study about primary biliary cirrhosis in which there are two outcomes of interest, the first of which is continuous and the second is binary.
6 citations
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TL;DR: In this article, a nonparametric Item Response Theory model for dichotomously scored items in a Bayesian framework is proposed, where parts of the items are defined on the basis of inequality constraints among the latent class success probabilities.
Abstract: We propose a nonparametric Item Response Theory model for dichotomously scored items in a Bayesian framework. Partitions of the items are defined on the basis of inequality constraints among the latent class success probabilities. A Reversible Jump type algorithm is described for sampling from the posterior distribution.A consequence is the possibility to make inference on the number of dimensions (i.e., number of groups of items measuring the same latent trait) and to cluster items when unidimensionality is violated.
5 citations
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TL;DR: 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.
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.
5 citations
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TL;DR: In this paper, a computationally convenient test for the null hypothesis of time-invariant individual effects is proposed, which is an application of Hausman (1978) specification test procedure and can be applied to generalized linear models for panel data, a wide class of models that includes the Gaussian linear model and a variety of nonlinear models typically employed for discrete or categorical outcomes.
Abstract: Recent literature on panel data has emphasized the importance of accounting for time-varying unobserved heterogeneity, which may stem either from time-varying omitted variables or macro-level shocks that affect each individual unit differently. In this paper, we propose a computationally convenient test for the null hypothesis of time-invariant individual effects. The proposed test is an application of Hausman (1978) specification test procedure and can be applied to generalized linear models for panel data, a wide class of models that includes the Gaussian linear model and a variety of nonlinear models typically employed for discrete or categorical outcomes. The basic idea is to compare fixed effects estimators defined as the maximand of full and pairwise conditional likelihood functions. Thus, the proposed approach requires no assumptions on the distribution of the individual effects and, most importantly, it does not require them to be independent of the covariates in the model. We investigate the finite sample properties of the test through a set of Monte Carlo experiments. Our results show that the test performs quite well, with small size distortions and good power properties. A health economics example based on data from the Health and Retirement Study is used to illustrate the proposed test.
1 citations
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TL;DR: In this paper, the authors proposed a strategy for ranking scientific journals starting from a set of available quantitative indicators that represent imperfect measures of the unobservable "value" of the journals of interest.
Abstract: We propose a strategy for ranking scientific journals starting from a set of available quantitative indicators that represent imperfect measures of the unobservable "value" of the journals of interest. After discretizing the available indicators, we estimate a latent class model for polytomous item response data and use the estimated model to classify each journal. We apply the proposed approach to data from the Research Evaluation Exercise (VQR) carried out in Italy with reference to the period 2004-2010, focusing on the sub-area consisting of Statistics and Financial Mathematics. Using four quantitative indicators of the journals' scientific value (IF, IF5, AIS, h-index), some of which not available for all journals, we derive a complete ordering of the journals according to their latent value. We show that the proposed methodology is relatively simple to implement, even when the aim is to classify journals into finite ordered groups of a fixed size. Finally, we analyze the robustness of the obtained ranking with respect to different discretization rules.
1 citations
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TL;DR: In this paper, a multidimensional latent class IRT model for binary items is proposed, in which the missingness mechanism is driven by a latent variable (propensity to answer) correlated with the latent variable for the ability (or latent variables for the abilities) measured by the test items.
Abstract: A relevant problem in applications of Item Response Theory (IRT) models is due to non- ignorable missing responses. We propose a multidimensional latent class IRT model for binary items in which the missingness mechanism is driven by a latent variable (propensity to answer) correlated with the latent variable for the ability (or latent variables for the abilities) measured by the test items. These latent variables are assumed to have a joint discrete distribution. This assumption is convenient both from the point of view of estimation, since the manifest distribution of the responses may be simply obtained, and for the decisional process, since individuals are classified in homogeneous groups having common latent variable values. Moreover, this assumption avoids parametric formulations for the distribution of the latent variables, giving rise to a semiparametric model. The basic model, which can be expressed in terms of a Rasch or a two-parameters logistic parameterization, is also extended to allow for covariates that influence the weights of latent classes. The resulting model may be efficiently estimated through the discrete marginal maximum likelihood method, making use of the Expectation-Maximization algorithm. The proposed approach is illustrated through an application to data coming from a Students’ Entry Test for the admission to the courses in Economics in an Italian University.
1 citations
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TL;DR: In this paper, a multidimensional framework based on an LC model was proposed to identify various types of corruption, which goes beyond the usual classification into administrative and political types of corrupt activities.
Abstract: Evaluation of corrupt activities is incrementally based
on administration of questionnaires to firms in business, and generally involves a large number of items.
Data collected
by questionnaires of this type can be analyzed by Latent Class (LC) models in order to
classify firms into homogeneous groups according to the perception of corruption.
In this paper, we propose a multidimensional framework, based on an LC model, to identify various types of corruption.
By using a dataset for transition economies, we identify four classes of corrupt activities, which go beyond
the usual classification into administrative and political types of corruption; we then validate
our estimates by using a direct administrative corruption index from the same dataset and by comparing, at country level, corruption perception rankings
published by Transparency International.
The potential of the proposed approach is illustrated
through an application to the relationship between firms' competitiveness and the identified latent corruption classes,
with evident heterogeneity in the interpretation of results regarding policy implications.
1 citations
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TL;DR: A composite likelihood approach based on considering different subsets of data is introduced for arrays of data, which allows for two factor clustering of the observed units when one dimension is referred to consecutive time occasions.
Abstract: We consider a discrete latent variable model for arrays of data, which allows for two factor clustering of the observed units when one dimension is referred to consecutive time occasions. The model then relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. Therefore, we introduce a composite likelihood approach based on considering different subsets of data. The proposed approach is illustrated by a simulation study and an application in genomics.
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04 Mar 2013
TL;DR: The work describes a multidimensional latent class Rasch model and its application to data about the measurement of some aspects of Health-related Quality of Life and Anxiety and Depression in oncological patients.
Abstract: “the individuals’ perceptions of their position in life in the context of their culture and the value systems in which they live, and in relation to their goals, expectations, standards and concerns. It is a broad-ranging concept affected in a complex way by the persons’ physical health, psychological state, level of independence, social relationships, personal beliefs, and their relationship to the salient features of their environment.”
01 Jan 2013
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TL;DR: The Adaptive Gaussian-Hermite (AGH) numerical quadrature approximation is proposed for a class of dynamic latent variable models for time-series and panel data and a comparison between the performance of AGH methods and alternative approximation methods proposed in the literature is carried out by simulation.
Abstract: Maximum likelihood estimation of dynamic latent variable models requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the Adaptive Gaussian-Hermite (AGH) numerical quadrature approximation for a class of dynamic latent variable models for time-series and panel data. These models are based on continuous time-varying latent variables which follow an autoregressive process of order 1, AR(1). Two examples of such models are the stochastic volatility models for the analysis of financial time-series and the limited dependent variable models for the analysis of panel data. A comparison between the performance of AGH methods and alternative approximation methods proposed in the literature
is carried out by simulation. Examples on real data are also used to illustrate the proposed approach.