Full Professor in Statistics, Department of Economics, University G. d’Annunzio of Chieti Pescara, Italy. Currently vice-coordinator of the Research group GRASPA for Statistical Applications to Environmental problems (http://www.graspa.org/).

MAIN RESEARCH INTERESTS

The research activity is mainly focused on the study of multivariate processes with temporal, spatial and spatio-temporal structures with interest in environmental applications, image and shape analysis. Specific research-topics of interest are:

1) Optimal spatial designs for environmental monitoring: monitoring networks are important to provide information on several environmental aspects such as air pollution, acid rain, water quality, earthquakes etc. For most environmental applications, we also require a prior mapping of the target pollutant agents over the study region so that the construction of an optimal monitoring network becomes a common problem with spatial dependence playing a crucial role. When a new network is to be constructed, or an existing one augmented or modified, it is important that the monitoring sites are optimally allocated across space to maximize the information available, which can then be used to make reliable and credible inferences about a variable of interest. When geostatistical data are considered, the monitoring network can be constructed to emphasize the utility of designs for interpolation. Assuming the second-order dependence is known, optimal interpolators (in the sense of minimum mean squared error) are jointly used with design criteria generally expressed as a function of the prediction variance. The specification of the variance matrix Σ, and the use of Gaussian Random Fields (GRFs), define an optimal interpolator known in geostatistics as kriging (Cressie, 1993). An alternative approach is to specify a Gaussian Markov Random Field (GMRF), or Gaussian conditional autoregression (Cressie, 1993), which essentially is based on the specification of Σ-1. The purpose of this project is twofold. Firstly, it aims at providing a framework for the optimal spatial interpolator in which the two forms of kriging and GMRFs can be used. Secondly, it attempts to provide new objective functions to be used for designing optimal sampling schemes for spatial predictions.

2) Hierarchical spatio-temporal models: the idea of borrowing information from different but related sources can be very powerful for statistical analysis. It proved to be very useful in the last decades where complex data structures began to be tackled, as they required sophisticated modeling strategies. The aim of this project is to develop hierarchical spatio-temporal models for the study of environmental complex phenomena and for health care research in general. Particular attention is devoted to the development of Bayesian Factor models and the related issues pertaining to the study of the relationships existing between groups of variables which are supposed to be spatially and temporally correlated . Current interest also rests on modeling ideas that engender parsimonious structures and, in particular, on approaches to inducing data-informed sparsity via full shrinkage to zero of (many) latent time-varying loadings. Bayesian sparsity modeling ideas are well-developed in static models, such as sparse latent factor and regression models, but mapping over to time series raises new challenges of defining general approaches to dynamic sparsity.

This project is part of the research project RBFR12URQJ entitled “Statistical modeling of environmental phenomena: pollution, meteorology, health and their interactions — StEPhI Project 2013. MIUR, Ministero dell’Istruzione, dell’Università e della Ricerca, Futuro in Ricerca. Grant: 637000 Euro.

3) Image processing and functional data analysis: the aim is to work with data which are in the form of images or multiple time-dynamic processes naturally described as functional. Gauss Markov Random fields are commonly used in the field of image analysis and here we consider issues concerned with parameter estimation, both for univariate and multivariate processes. As for functional data, we consider curves that have a natural hierarchical structure. That is, we assume that at the top level of the hierarchy there are treatment groups and then, within these treatment groups, there are experimental or sampling units and, nested within these units, we may also find other levels of hierarchy with corresponding sub-units. Data showing such a hierarchical structure are common, for example, in electro-encephalography, magneto-encephalography (Kami_nski et al., 2005) and thermal infrared - IR - imaging studies. These curves can share strong physical relationships and can thus be assumed correlated at their deepest possible level. In this respect, we thus consider issues concerned with the development of models which are able to disentangle the nested structure of multiple signals and the specification of covariance structures for sub-unit level functions.

4) Dynamic Shape analysis: the shape of an object is the geometrical information remaining after the effects of changes in location, scale and orientation have been removed. Statistical analysis of dynamic shapes is a problem with significant challenges due to the difficulty in providing a description of the shape changes over time, across subjects and over groups of subjects. Recent attempts to study the shape change in time are based on the Procrustes tangent coordinates or spherical splines in Kendall shape spaces. In this framework we investigate the use of basis functions, defined by principal warps in space and time, to facilitate the development of a spatio-temporal model which is able to describe the time-varying deformation of the ambient space in which the objects of interest lie.

However, for this project, we also deal with the statistical analysis of a temporal sequence of landmark data using the exact distribution theory for the shape of planar correlated Gaussian configurations. Specifically, we aim at extending the theory of the offset-normal distribution to a dynamic framework and discuss its use for the description of time-varying shapes. Modeling the temporal correlation structure of the dynamic process is a complex task, in general. For two time points, Mardia and Walder (1994) have shown that the density function of the offset-normal distribution has a rather complicated form and discussed the difficulty of extending their results to t>2. For this project, we aim at showing that, in principle, it is possible to calculate the closed form expression of the offset-normal distribution for a general t, though its calculation can be computationally expensive.

5) Regional disparities in economic and social phenomena in Europe: there is now a clear recognition that the major societal challenges faced by the EU today, such as economic competitiveness, social cohesion, income poverty and inequality, health inequalities, environmental challenges as well as energy security and use, have different impacts in different regions and different requirements to cope with the economic and social consequences of crisis situations. Despite the effort of many EU programs, the territorial dimension in policy development is still hampered by the lack of systematic, comparable and reliable European territorial strategies. The assessment of the potential impacts of EU initiatives is still at times overlooked, since it often fails to take into account the spatial and temporal dimensions of phenomena of interest and recognize the territorially heterogeneous nature of impacts within and between EU member states. By examining a range of socio-economic outcomes, the overall objective of this project is to detect, expose and compare the territorial specificities of EU in order to identify comparable regions and different territorial development processes. We aim to detect key trends from the local to the global level so that regions can identify their challenges and potentials, compare with other regions sharing their issues and exploit the diversity of growth opportunities.

Recent published papers:

Fontanella, Lara, Ippoliti, Luigi, Valentini, Pasquale (2019). Predictive functional ANOVA models for longitudinal analysis of mandibular shape changes. BIOMETRICAL JOURNAL, vol. -, p. 1-16, ISSN: 0323-3847, doi: 10.1002/bimj.201800228

Fontanella, Lara, Ippoliti, Luigi, Kume, Alfred (2019). The Offset Normal Shape Distribution for Dynamic Shape Analysis. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, vol. -, p. 1-41, ISSN: 1061-8600, doi: 10.1080/10618600.2018.1530118

Ippoliti, L., Martin, R. J., ROMAGNOLI, Luca (2018). Efficient likelihood computations for some multivariate Gaussian Markov random fields. JOURNAL OF MULTIVARIATE ANALYSIS, vol. 168, p. 185-200, ISSN: 0047-259X, doi: 10.1016/j.jmva.2018.07.007

Bruno, Francesca, Cameletti, Michela, Franco Villoria, Maria, Greco, Fedele, Ignaccolo, Rosaria, IPPOLITI, Luigi, VALENTINI, PASQUALE, Ventrucci, Massimo (2016). A survey on ecological regression for health hazard associated with air pollution. SPATIAL STATISTICS, vol. 18, p. 276-299, ISSN: 2211-6753, doi: 10.1016/j.spasta.2016.05.003

1. Brombin C., Salmaso L., Fontanella L., Ippoliti L. (2015). Nonparametric combination-based tests in dynamic shape analysis. JOURNAL OF NONPARAMETRIC STATISTICS, vol. 27, p. 460-484, ISSN: 1048-5252, doi: 10.1080/10485252.2015.1071811

2. FONTANELLA L., IPPOLITI L., SARRA A., VALENTINI P., PALERMI S. (2015). Hierarchical Generalised Latent Spatial Quantile Regression Models with Applications to Indoor Radon Concentration. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, vol. 29, p. 357-367, ISSN: 1436-3240, doi: 10.1007/s00477-014-0917-0

3. Ippoliti L., Di Zio S., Merla A. (2014). Classification of biomedical signals for differential diagnosis of Raynaud's phenomenon. JOURNAL OF APPLIED STATISTICS, vol. , p. 1-18, ISSN: 0266-763, doi: 10.1080/02664763.2014.894002 –

4. FONTANELLA L., IPPOLITI L., SARRA A., VALENTINI P. (2013). Spatial Growth regressions for the convergence analysis of renewable energy consumption in Europe . STATISTICA, vol. 73, p. 39-53, ISSN: 1973-2201, doi: 10.6092/issn.1973-2201/3984

5. Ippoliti L., Martin R.J., Bhansali R.J. (2013). Rational spectral density models for lattice data. SPATIAL STATISTICS, vol. 6, p. 91-108, ISSN: 2211-6753, doi: 10.1016/j.spasta.2013.09.001 -

6. Ippoliti L., Romagnoli L., Arbia G. (2013). A Gaussian Markov random field approach to convergence analysis. SPATIAL STATISTICS, vol. 6, p. 78-90, ISSN: 2211-6753, doi: 10.1016/j.spasta.2013.07.005 –

7. VALENTINI P., IPPOLITI L., FONTANELLA L. (2013). MODELING US HOUSING PRICES BY SPATIAL

DYNAMIC STRUCTURAL EQUATION MODELS. THE ANNALS OF APPLIED STATISTICS, vol. 7, p. 763-798, ISSN:1932-6157, doi: 10.1214/12-AOAS613 –

8. FONTANELLA L., IPPOLITI L., MERLA A. (2012). Multiresolution Karhunen-Loève Analysis of Galvanic Skin Response for Psycho-Physiological Studies. METRIKA, vol. 75, p. 287-309, ISSN: 0026-1335, doi: 10.1007/s00184-010-0327-3

9. Ippoliti L., Valentini P., Gamerman D. (2012). Space–time modelling of coupled spatiotemporal environmental variables. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, vol. 61, p. 175-200, ISSN: 0035-9254, doi: 10.1111/j.1467-9876.2011.01025.x –

10. FONTANELLA L., IPPOLITI L. (2012). Karhunen-Loève Expansion of Temporal and Spatio-Temporal Processes. In: T.Subba Rao, S.Subba Rao, C.R.Rao. Handbook of Statistics: Time Series Analysis: Methods and Applications. p. 497-522, Chennai :Elsevier , ISBN: 9780444538581, doi: 10.1016/B978-0-444-53858-1.00017-X

11. Ippoliti L., Martin R.J., Bhansali R.J. (2011). Discussion of the paper “An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach". by Lindgren et al (2011). JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B STATISTICAL METHODOLOGY, p. 475-477, ISSN: 1369-7412 -