Ricerc@Sapienza

In analytic inference, data usually come from complex sampling designs, possibly with different inclusion probabilities, stratification, of units. The effect of a complex sampling design is that sampling data are not {\em i.i.d.}, even if they are at a superpopulation level. This dramatically changes the probability distribution of usual test-statistics, such as Spearman's Rho. An approach based on a special form of resampling is proposed, and its properties are studied.

Resampling methods are widely studied and increasingly employed in applied
research and practice. When dealing with complex sampling designs, common
resampling techniques require to adjust non-integer sampling weights in order to
construct the so called “pseudo-population” where to perform the actual resampling.
In particular, to lighten the computational burden, it is commonly suggested
to round resampling weights to the nearest integer. This practice, however, has
been empirically shown to be harmful under general designs. Here we develop

In this paper, a class of resampling techniques for finite populations under pps sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in: (i) constructing a pseudo-population" on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the "sampling design level". Theoretical justifications based on large sample theory are provided.

In this paper, a new resampling technique for sampling designs with unequal inclusion probabilities is proposed. The basic idea is to use a resampling design based on ppswor. Its main properties are studied, and its relationships with other resampling methodologies are discussed.

This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with different inclusion probabilities. A (design-based) Hájek type estimator for the Lorenz curve is proposed, and its asymptotic properties are studied. Then, a resampling scheme able to approximate the asymptotic law of the Lorenz curve estimator is constructed.

This communication is devoted to resampling based on pseudo-populations for a class of two-stage sampling design. A resampling scheme is defined, and its main theoretical properties are established.

A measure of interrater absolute agreement for ordinal scales is proposed capitalizing on the dispersion index for ordinal variables proposed by Giuseppe Leti. The procedure allows to overcome the limits affecting traditional measures of interrater agreement in different fields of application. An unbiased estimator of the proposed measure is introduced and its sampling properties are investigated. In order to construct confidence intervals for interrater absolute agreement both asymptotic results and bootstrapping methods are used and their performance is evaluated.