Small area estimation, Spatial correlation, SAR model, Spatial EBLUP, Lattice data,

The objective of this study is to investigate whether the quality of educational services and the university's institutional image influence students' overall satisfaction with their university experience as well as the possible consequences of these relationships on students' loyalty. In particular, in today's increasingly competitive higher education environment, such concepts have become of strategic concern in both public and private universities. To explain the complex system of relationships among these constructs, several hypotheses were formulated and tested through a structural equation model (SEM). Data were collected through a web questionnaire handed out to 14,870 students enrolled at the University of Pisa. The results provide valuable insight and show that teaching and lectures and teaching and course organization are the main determinants of students' satisfaction and students' loyalty among the more academic components of the educational service. Furthermore, the crucial role played by university image is worth noting, both for its direct and indirect effects on students' satisfaction as well as on students' loyalty and on teaching and lectures.

The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

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Spatial correlation, Simultaneously autoregressive process, Small area estimation, Parametric bootstrap, Non-parametric bootstrap, 62D05, 62J05,

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Abstract.One popular approach to small area estimation when data are spatially correlated is to employ Simultaneous Autoregressive (SAR) random effects models to define the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP). See Singh et al. (2005) and . SAR models allow for spatial correlation in the error structure. An alternative approach that incorporates the spatial information in the regression model is to use Geographically Weighted Regression (GWR). See Brunsdon et al. (1996) and Fotheringham et al. (1997). GWR extends the traditional regression model by characterising the relationship between the outcome variable and the covariates via local rather than global parameters. In this paper we investigate GWR-based small area estimation under the M-quantile modelling approach (Chambers and Tzavidis, 2006). In particular, we integrate the concepts of outlier-robust small area estimation and borrowing strength over space within a unified modelling framework by specifying an M-quantile GWR model that is a local model for the M-quantiles of the conditional distribution of the outcome variable given the covariates. This model is then used to define an outlier-robust predictor of the small area characteristic of interest that also accounts for spatial association in the data. An additional important spin-off from applying the M-quantile GWR small area model is more efficient synthetic estimation for out of sample areas. We demonstrate the usefulness of this framework through both model-based as well as design-based simulation, with the latter based on a realistic survey data set. The paper concludes with an application to environmental data for predicting average levels of the Acid Neutralizing Capacity at 8-digit Hydrologic Unit Code level in the Northeast states of the U.S.A.