Copula-based predictions in small area estimation

Unit-level regression models are commonly used in small area estimation (SAE) to obtain an empirical best linear unbiased prediction of small area characteristics. The underlying assumptions of these models, however, may be unrealistic in some applications. Previous work developed a copula-based SAE model where the empirical Kendall's tau was used to estimate the dependence between two units from the same area. In this article, we propose a likelihood framework to estimate the intra-class dependence of the multivariate exchangeable copula for the empirical best unbiased prediction (EBUP) of small area means. One appeal of the proposed approach lies in its accommodation of both parametric and semi-parametric estimation approaches. Under each estimation method, we further propose a bootstrap approach to obtain a nearly unbiased estimator of the mean squared prediction error of the EBUP of small area means. The performance of the proposed methods is evaluated through simulation studies and also by a real data application.     

A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals

Statistics and Computing - This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series...     

Signed compound poisson integer-valued GARCH processes

Abstract

We propose signed compound Poisson integer-valued GARCH processes for the modeling of the difference of count time series data. We investigate the theoretical properties of these processes and we state their ergodicity and stationarity under mild conditions. We discuss the conditional maximum likelihood estimator when the series appearing in the difference are INGARCH with geometric distribution and explore its finite sample properties in a simulation study. Two real data examples illustrate this methodology.     

Identity and Disruptiveness in Boys: Longitudinal Perspectives

After more than 30 years of research on identity, the links between identity and disruptive behaviors are not clear. This study compared the identity formation of boys with stable disruptive behaviors from age 6 to age 15 (n = 16), with the identity formation of boys that were never disruptive during the same period (n = 25). All boys came from low socio-economic status families. At age 9 and 11, identity was assessed with the The Self-Perception Profile for Children (Harter, 1985). At age 15, identity was assessed with the Extended Objective Measure of Ego Identity Status (Adams, Bennion, & Huh, 1989) and two scales of the Offer Self-Image Questionnaire (Offer, Ostrov, & Howard, 1981). Differences between disruptive and non-disruptive boys were found only for behavior in childhood and identity in ideological domains at age 15. Explanations for the weakness of the link between identity and disruptiveness are discussed.     

Estimation of symmetric disagreement using a uniform association model for ordinal agreement data

The Cohen kappa is probably the most widely used measure of agreement. Measuring the degree of agreement or disagreement in square contingency tables by two raters is mostly of interest. Modeling the agreement provides more information on the pattern of the agreement rather than summarizing the agreement by kappa coefficient. Additionally, the disagreement models in the literature they mentioned are proposed for the nominal scales. Disagreement and uniform association models are aggregated as a new model for the ordinal scale agreement data, thus in this paper, symmetric disagreement plus uniform association model that aims separating the association from the disagreement is proposed. Proposed model is applied to real uterine cancer data.     

Stability and Change in Kindergartners' Friendships: Examination of Links with Social Functioning

Stability and change in kindergartners' friendships were examined based on a typology of profiles (stable, fluid, loss, gain, friendless). Our purpose was to determine whether children belonging to the five profiles differed in their social functioning. The sample was composed of 2353 kindergartners. Reciprocal friendships and social functioning indices were measured using peer nominations collected in October and May of the same year. A series of repeated measures analysis of variance indicated that children in the friendless profile were less accepted by their peers, more shy, more withdrawn, and more aggressive than other children. Moreover, children in the stable profile were significantly more accepted by their peers, more prosocial, and less shy than children in the fluid profile. Lastly, children in the loss profile became less accepted by their peers and less prosocial over the course of the year whereas children in the gain profile became more accepted and prosocial.     

On tail index estimation based on multivariate data

This article is devoted to the study of tail index estimation based on i.i.d. multivariate observations, drawn from a standard heavy-tailed distribution, that is, of which Pareto-like marginals share the same tail index. A multivariate central limit theorem for a random vector, whose components correspond to (possibly dependent) Hill estimators of the common tail index α, is established under mild conditions. We introduce the concept of (standard) heavy-tailed random vector of tail index α and show how this limit result can be used in order to build an estimator of α with small asymptotic mean squared error, through a proper convex linear combination of the coordinates. Beyond asymptotic results, simulation experiments illustrating the relevance of the approach promoted are also presented.     

Bootstrap Prediction Intervals for Factor Models

We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the ordinary least-squares (OLS) estimator.