A new matching procedure based on imputing missing data by means of a local linear estimator of the underlying population regression function (that is assumed not necessarily linear) is introduced. Such a procedure is compared to other traditional approaches, more precisely hot deck methods as...

The aim of the paper is to evaluate the matching noise produced by nonparametric imputation techniques referring to the kNN method, both with fixed and variable number of donors k. The matching noise is evaluated formally and via a simulation.

Bayesian networks are multivariate statistical models using a directed acyclic graph to represent statistical dependencies among variables. When dealing with Bayesian Networks it is common to assume that all the variables are discrete. This is not often the case in many real contexts where also...

In this paper Object-Oriented Bayesian networks are proposed as a tool to model measurement errors in a categorical variable due to respondent. A mixed measurement error model is presented and an Object-Oriented Bayesian network implementing such a model is introduced. The insertion of evidence...

In this paper we consider a nonparametric sequential test of power one for the Andersen risk model. The main motivation comes from applications to insurance, and in particular to the sequential control of the ruin probability of an insurance company. The properties of the proposed test are...

This paper provides a survey of the non parametric approaches applied to income distribution analysis, focusing on kernel method and, in particular, on the choice of the bandwidth. To account for sample design and data sparseness, weighted kernel density with an adaptive bandwidth is suggested....