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Pievani & Serrelli (2011) assessed how "exaptation" has been welcomed in the various fields of human evolution. The present essay expands the review to works that address articulate language and come to "exaptive" explanations of its origin. With the occasion, we remark methodologically the core of exaptation, i.e. change of function, as distinct from other correlated problems, such as abruptness of change or discontinuity of its products. A useful analogy to understand these aspects is Roger Lass's approach to "morphological junk" in linguistics. In the conclusion, we sketch out a general outlook on the analyzed wotks. They converge upon the more and more corroborated and detailed hypothesis that articulated language, like many other complex characters, is a further example of evolutionary bricolage. Studying language in this perspective can still reach many results

In recent years, much research has been devoted to solve the problem of missing data imputation. Although most of the novel proposals look attractive for some reason, less attention has been paid to the problem of when and why a particular method should be chosen while discarding the others. This matter is far crucial in applications, given that unsuitable solutions could heavily affect the reliability of statistical analyses. Starting from this, this work is addressed to study how well several algorithmic-type imputation methods perform in the case of quantitative data. We focus on three different logics of imputing, based respectively on the use of random forests, iterative PCA, and the forward procedure. In particular, the latter, having initially been introduced for ordinal data, has required us to develop an original adaptation so that it handles missing quantitative values.

The empirical validation of the analytical properties of sampling allocation methods is based on simulation techniques either for the whole population or for domain analysis, or for other fields of statistics. At almost negligible costs, these techniques allow for checking general properties (even asymptotic) of estimators or statistical models. The aim of this paper is to propose a simulation sampling technique – namely a stratified sampling with an ex-post ordered selection - for the detection of the most effective sampling allocation in terms of precision of estimates. More specifically, in order to compare several allocation methods, it is necessary to make unique and efficient the sampling experiment (from a computational point of view). To this aim the proposed technique is based on: i) the separation between process steps – namely the selection and the allocation steps, and the inference step; ii) the separation between strata and more general domains; iii) the definition of the selection size by stratum or domain of analysis (i.e. the detection of the maximum selection size for multiple allocation methods); iv) the ex-post random assignment of labels to the list of unit replicates (in order to prevail against the loss of efficiency bounded to ordered selections), regardless of the selected strata definition. In this way an ordered selection of units is enabled. This selection is also efficient and suitable for a simultaneous comparison of the various allocation methods considered. A case study on ISAE-ISTAT Business Tendency Survey on manufacturing sample is presented.

The globalization of economy requires a steadily increasing demand for statistical information. Unification political processes and the establishment of international institutions are among the events which have contributed to such increase during the last decades. The evolution of the Business Confidence Survey on manufacturing sector is presented starting from the preliminary European project for harmonised statistics launched in the late fifties of the last century. Survey changes are described, focusing in particular on the so-called confidence indicator. The continuing increase of statistical accuracy in sampling is recalled, from the initial purposive sample and controls up to the present state of the art. Specific attention is devoted to the role of administrative archives in the sampling plan. Emphasis is also given to the increasing use of computer simulation in assessing the validity of the estimates. The role of cyclical analysis is finally highlighted with regard to two aspects: (i) the business confidence has not a corresponding variable in the economic system - the validation can only be performed in comparison with correlated variables (e.g. IP, GDP); (ii) confidence shows forecasting capability for the economic system

Il saggio propone un'analisi critica dell'ICF e della sua applicabilità nel contesto scolastico.

After the full implementation of the new EU Standard Classification of Economic Activities (Nace Rev.2) in 2008, statistical agencies have increasingly dealt with the problem of redefining sampling designs and estimation techniques, especially in the case of stratified surveys with NACE codes as stratification variables. In light of this changes, the Italian Institute for Studies and Economic Analysis (Istituto di Studi e Analisi Economica - ISAE) is currently updating the sample design of its Business Tendency Survey (BTS). The focus of this paper is on finding a strata allocation methodology suitable to overcome the NACE Rev.2 changes. The analysis is carried out by considering two opposite needs: i) the strata allocation must retain multiple information; ii) the strata allocation must retain the optimality of the estimates. The allocation methods considered are: i) the classical Neyman x-optimal allocation, ii) the Neyman allocation used by ISAE, i.e. with direct application to areal stratification, iii) the multivariate Neyman allocation on qualitative variance according to Bethel formulation, iv) the Robust Optimal Allocation with Uniform Stratum Threshold (ROAUST). The ROAUST is a new allocation method which generates a new class of stratified estimators. Comparison among these methods is carried out via a simulation device - the Sequential Selection-Allocation (SSA). This simulation device constructs a new population list with units re-labelled within each stratum, such that the new labels corresponds to the order of selection in a SWOR resampling of the stratum units. This process is repeated a certain number N (N=1,000 in the simulation presented in this paper) of times. From this new labelled population, all the allocation algorithms can be evaluated simultaneously.

Neyman’s algorithm for the allocation of sample units in business sampling can result unsatisfactory in domain analysis with imperfect frames and sectorial and/or regional data. Improved estimates can be obtained using stratified estimators combined with an optimal unit allocation. We achieve this outcome by an interdisciplinary approach which leads to a methodological improvement. Starting from Martini’s approach which considers an empirical view of the statistical analysis, we propose the Robust Optimal Allocation with Uniform Stratum Threshold (ROAUST) class of stratified estimators and prove their reliability by using a simulation approach inspired by Magagnoli’s work on this issue. In particular, contrary to Neyman’s stratified estimator with optimal allocation and stratum threshold, our class guarantees better domain representativeness.

In finite population sampling, when dealing with negligible sampling fractions (due, for example, to budget restrictions), or when stratification criteria definitions are not statistical in nature (e.g. heterogeneous administrative settings), or when data quality is not satisfactory (for example, when frame lists are out of date, or auxiliary information is required), a rethinking of Tschuprow’s (1924) optimality concept (later extended by Neyman, 1934) in stratified sampling is needed. In fact, the need of stratum representativeness from one side, and the optimum allocation from the other are often in conflict. Furthermore, the choice of a sampling design that rigorously obeys to the laws of standard statistical theory is a difficult task in practice, especially when knowledge barriers and operational constraints are present. Sometimes a purposive design is the only possibility. This work aims at finding the most suitable allocation method for a finite population of enterprises by taking into account the above-mentioned restrictions, and is carried out through a simulation approach which compares different methodologies. We consider, among others and together with the popular optimal allocation method, the ISAE (Institute for Studies and Economic Analysis) methodology (Martelli, 1998; Malgarini, Margani, and Martelli, 2005) and the Optimum Robust Allocation with Uniform Stratum Threshold (AORSU) method (Chiodini, Manzi, and Verrecchia, 2008). These methods fit both with domain analyses and in relation to the improvement of the estimates which are obtained through a proxy of the stratum variability. In general, the methods considered are useful when an ex-ante allocation is possible. Simulation studies are carried out on the ASIA – ISTAT database (the Italian Register of enterprises).

Resampling methods are often invoked in risk modelling when the stability of estimators of model parameters has to be assessed. The accuracy of variance estimates is crucial since the operational risk management affects strategies, decisions and policies. However, auxiliary variables and the complexity of the sampling design are seldom taken into proper account in variance estimation. In this paper bootstrap algorithms for finite population sampling are proposed in presence of an auxiliary variable and of complex samples. Results from a simulation study exploring the empirical performance of some bootstrap algorithms are presented.