Silvia Golia

A Resampling Algorithm for Chaotic Time Series

In the field of chaotic time series analysis, there is a lack of a distributional theory for the main quantities used to characterize the underlying data generating process (DGP). In this paper a method for resampling time series generated by a chaotic dynamical system is proposed. The basic idea is to develop an algorithm for building trajectories which lie on the same attractor of the true DGP, that is with the same dynamical and geometrical properties of the original data. We performed some numerical experiments on some short noise-free and high-noise series confirming that we are able to correctly reproduce the distribution of the largest finite-time Lyapunov exponent and of the correlation dimension.

A proposal for categorizing the severity of non uniform differential item functioning: The polytomous case

Abstract Differential item functioning (DIF) is present when something about the characteristics of a test taker interferes with the relationship between ability and item response. Non uniform DIF (NUDIF) exists when there is interaction between ability level and group membership. The aim of this study is to propose guidelines to evaluate the severity of NUDIF, taking into account the family of Rasch models. The severity of NUDIF is evaluated trying to quantify the overall biasing impact of the presence in the test of NUDIF items, on the estimated measure of the latent trait of interest.

Evaluating the GPH Estimator via Bootstrap Technique

In this work a bootstrap method for long-memory processes is presented. The method is based on a combination of the discrete wavelet transform and the stationary bootstrap and is a development of the one presented by Percival et al. (2001). This bootstrap scheme is empirically tested calculating the GPH estimator for fractional gaussian noise time series. The set of bootstrap estimations is compared with the set of estimations obtained via MonteCarlo simulation procedure.

The Effects of the Presence of Items Affected by Uniform Differential Item Functioning on Rasch Measure

Differential item functioning (DIF) is understood to be present when something about the characteristics of a test taker interferes with the relationship between ability and item responses. For a given level of trait, the probability of endorsing a specified item response should be independent of subgroup membership; if it does not happen, then that item is said to exhibit DIF. When DIF items are present in the test, an impact on the estimated ability measure could be expected. The present simulation study addresses the issue in assessment of the impact of uniform DIF on the measures obtained applying the Rasch model when the questionnaire is formed by polytomous items.