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In the field of educational measurement, it often happens that tests consist of different subscales involving explicitly several dimensions. With the aim of estimating the test item psychometric properties and the candidate ability, item response theory (IRT) models are often used with multidimensional structure. In this work, a comparison among multidimensional models with different ability structures is conducted in a case study. In particular, data from Italian student assessments conducted at the end of the lower secondary school by INVALSI are taken into account.

We discuss two methods for clustering financial time series in extreme scenarios. The procedures are based on the calculations of two different measures of tail dependence, namely the (lower) tail dependence coefficient and the conditional Spearman’s correlation. Performances of the proposed methodologies are compared via a simulation study.

The aim of the paper is to establish asymptotics in sampling finite populations. Asymptotic results are first established for an analogous of the empirical process based on the Hajek estimator of the population distribution function, and then extended to Hadamard-differentiable functions. As an application, asymptotic normality of estimated quantiles is provided.

In this paper we analyze the problem of testing for the presence of unit roots and cointegration in the case of macro-economic and financial time series. Traditional units test are not suitable for these series as they present permanent volatility shifts, so different tests have been proposed. We consider a wild bootstrap version of the M unit test. Instead of standard normal bootstrap residuals Student-t bootstrap residuals are used. Some simulations been performed comparing the results obtained using both type of bootstrap residuals. The application regards to the MSCI Equity Indices.

Within the framework of recursive partitioning algorithms by tree-based methods, this paper provides a contribution on both the visual representation of the final data partition in a geometrical space and the selection of the decision tree. The results in terms of error rate are really similar to the ones returned by the Classification And Regression Trees procedure, showing how this novel way to select the best tree is a valid alternative to the well know cost-complexity pruning.