1,721,046 research outputs found
Accordo assoluto tra valutazioni espresse su scala ordinale
No full textMany methods for measuring agreement among raters have been proposed and applied in many domains in the areas of education, psychology, sociology, and medical research. A brief overview of the most used measures of interrater absolute agreements for ordinal rating scales is provided, and a new index is proposed that has several advantages. In particular, the new index allows to evaluate the agreement between raters for each single case (subject or object), and to obtain also a global measure of the interrater agreement for the whole group of cases evaluated. The possibility of having evaluations of the agreement on the single case is particularly useful, for example, in situations where the rating scale is being tested, and it is necessary to identify any changes to it, or to request the raters for a specific comparison on the single case in which the disagreement occurred. The index is not affected by the possible concentration of ratings on a very small number of levels of the ordinal scale
Modeling measurement error via nonparametric Bayesian belief nets
No full textMeasurement error is the difference between the value provided by the respondent and the true (but unknown) value. It is sometimes defined as observation error, since it is related to the observation of the variable at the data collection stage. The problem of measurement error in financial assets is studied. The measurement error is modeled by means of non parametric Bayesian belief networks, that are graphical models expressing the dependence structure through bivariate copulas associated to the edges of the graph without introducing any distributional assumption. A new error correction procedure based on non parametric Bayesian belief networks is proposed. Measurement error modeling and microdata correction are illustrated by means of an application to the Banca d’Italia Survey on Household Income and Wealth 2008. The measurement model and its parameters have been estimated via a validation sample. The sensitivity of the conditional distribution of the true value given the observed one to different evidence configurations is analysed
PC algorithm for complex survey data via resampling
No full textThe PC algorithm is one of the main methods for learning the structure of a Bayesian network from sample data. The algorithm uses conditional independence tests for model selection in graphical modeling and it is based on assumption of independent and identically distributed observations (i.i.d). The i.i.d. assumption is almost never valid for sample surveys data since most of the commonly used survey designs employ stratification and/or cluster sampling and/or unequal selection probabilities. The impact of complex design on i.i.d. based procedures can be very severe leading to erroneous results, then alternative procedures are needed which allow for complex designs. The aim is to modify the PC algorithm using resampling methods for finite population in order to take into account the complexity of sampling design in the learning process
Campionamento da popolazioni finite
No full textIl volume tratta aspetti recenti del campionamento da popolazioni finit
Inference for quantiles of a finite population: asymptotic versus resampling results.
No full textThe aim of the paper is to study the problem of estimating the quantile function of a finite population. Attention is first focused on point estimation, and asymptotic results are obtained. Confidence intervals are then constructed, based on both the following: (i) asymptotic results and (ii) a resampling technique based on rescaling the ‘usual’ bootstrap. A simulation study to compare asymptotic and resampling-based results, as well as an application to a real population, is finally performed
Measuring uncertainty in statistical matching
No full textAn important feature of statistical matching is that the underlying joint distribution of the variables of interest is not identifiable. This produces a form on “uncertainty” on the statistical model. A measure to evaluate such an uncertainty is proposed. The effect of prior information in the form of constraints is exploited. Finally, the estimation of the proposed measure of uncertainty is studied
Errors depending on costs in sample surveys
Full text link"This paper presents a total survey error model that simultaneously treats sampling error, nonresponse error and measurement error. The main aim for developing the model is to determine the optimal allocation of the available resources for the total survey error reduction. More precisely, the paper is concerned with obtaining the best possible accuracy in survey estimate through an overall economic balance between sampling and nonsampling error." (author's abstract
- …
