Ricerc@Sapienza

Choice behavior and preferences typically involve numerous and subjective aspects that are difficult to be identified and quantified. For this reason, their exploration is frequently conducted through the collection of ordinal evidence in the form of ranking data. Multistage ranking models, including the popular Plackett-Luce distribution (PL), rely on the assumption that the ranking process is performed sequentially, by assigning the positions from the top to the bottom one (forward order).

Multistage ranking models, including the popular Plackett-Luce distribution (PL), rely on the assumption that the ranking process is performed sequentially, by assigning the positions from the top to the bottom one (forward order). A recent contribution to the ranking literature relaxed this assumption with the addition of the discrete-valued reference order parameter, yielding the novel Extended Plackett-Luce model (EPL). Inference on the EPL and its generalization into a finite mixture framework was originally addressed from the frequentist perspective.

The PLMIX package offers a comprehensive framework aimed at endowing the R sta- tistical environment with some recent methodological advances in modeling and clustering partially ranked data.

In Bayesian decision theory, the performance of an action is measured by its posterior expected loss. In some cases it may be convenient/necessary to use a non-optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in one-sided testing.

In Bayesian inference, the two most widely used methods for set estimation of an unknown one-dimensional parameter are equal-tails and highest posterior density intervals. The resulting estimates may be quite different for specific observed samples but, at least for standard but relevant models, they tend to become closer and closer as the sample size increases. In this article we propose a pre-posterior method for measuring the progressive alignment between these two classes of intervals and discuss relationships with the skewness of the posterior distribution.

In Bayesian decision theory, the performance of an action is measured by its pos- terior expected loss. In some cases it may be convenient/necessary to use a non- optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in a general testing set-up.

The two most commonly used methods for Bayesian set estimation of an unknown one-dimensional parameter are equal-tails and highest posterior density intervals. The resulting estimates may be numerically different for specific observed samples but they tend to become closer and closer as the sample size increases. In this article we consider a pre-posterior measure of the progressive overlap between these two types of intervals and relationships with the skewness of the posterior distribution.

In Bayesian analysis of clinical trials data, credible intervals are widely used for inference on unknown parameters of interest, such as treatment effects or differences in treatments effects. Highest Posterior Density (HPD) sets are often used because they guarantee the shortest length. In most of standard problems, closed-form expressions for exact HPD intervals do not exist, but they are available for intervals based on the normal approximation of the posterior distribution.

This paper adds to the large body of literature on the effects of technology shocks
empirically and theoretically. Using a structural vector error correction model, we first
provide evidence that not only hours but also investment decline temporarily following a
technology improvement. This result is robust to important data and identification issues
addressed in the literature. We then show that the negative response of inputs is consistent
with an estimated monetary model in which the presence of strategic complementarity in