References of "Frasso, Gianluca"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailP-splines based clustering as a general framework: Some applications using different clustering algorithms
Iorio, Carmela; Frasso, Gianluca ULiege; D’Ambrosio, Antonio et al

in Mola, Francisco; Conversano, Claudio; Vichi, Maurizio (Eds.) Classification, (Big) Data Analysis and Statistical Learning (2018)

A parsimonious clustering method suitable for time course data applications has been recently introduced. The idea behind this proposal is quite simple but efficient. Each series is first summarized by ... [more ▼]

A parsimonious clustering method suitable for time course data applications has been recently introduced. The idea behind this proposal is quite simple but efficient. Each series is first summarized by lower dimensional vectors of P-spline coefficients and then, the P-spline coefficients are partitioned by means of a suitable clustering algorithm. In this paper, we investigate the performance of this proposal through several applications showing examples within both hierarchical and non-hierarchical clustering algorithms. © Springer International Publishing AG 2018. [less ▲]

Detailed reference viewed: 24 (2 ULiège)
Full Text
Peer Reviewed
See detailA P-spline based clustering approach for portfolio selection
Iorio, C.; Frasso, Gianluca ULiege; D'Ambrosio, A. et al

in Expert Systems with Applications (2018), 95

In the last years, many clustering techniques dealing with time course data have been proposed due to recent interests in studying phenomena that change over time. A new clustering method suitable for ... [more ▼]

In the last years, many clustering techniques dealing with time course data have been proposed due to recent interests in studying phenomena that change over time. A new clustering method suitable for time series applications has been recently proposed by exploiting the properties of the P-splines approach. This semi-parametric tool has several advantages, i.e. it facilitates the removal of noise from time series and it ensures a computational time saving. In this paper, we propose to use this clustering approach on financial data with the aim of building a financial portfolio. Our proposal works directly on time series without any pre-processing, except for the computation of the spline coefficients and, eventually, normalizing the series. We show that our strategy is useful to support the investment decisions of financial practitioners. © 2017 Elsevier Ltd [less ▲]

Detailed reference viewed: 13 (1 ULiège)
Full Text
Peer Reviewed
See detailChanges in brain structure after severe brain Injury
Annen, Jitka ULiege; Frasso, Gianluca ULiege; Heine, Lizette ULiege et al

Poster (2017)

Detailed reference viewed: 9 (4 ULiège)
Full Text
Peer Reviewed
See detailFunction–structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET
Annen, Jitka ULiege; Heine, Lizette ULiege; Ziegler, Erik ULiege et al

in Human Brain Mapping (2016), 37(11), 3707-3720

Detailed reference viewed: 102 (38 ULiège)
Full Text
Peer Reviewed
See detailInference in dynamic systems using B-splines and quasilinearized ODE penalties
Frasso, Gianluca ULiege; Jaeger, Jonathan; Lambert, Philippe ULiege

in Biometrical Journal (2016), 58(3), 691-714

Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems.We propose a smoothing approach regularized by a quasilinearized ... [more ▼]

Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems.We propose a smoothing approach regularized by a quasilinearized ODE-based penalty. Within the quasilinearized spline-based framework, the estimation reduces to a conditionally linear problem for the optimization of the spline coefficients. Furthermore, standard ODE compliance parameter(s) selection criteria are applicable.We evaluate the performances of the proposed strategy through simulated and real data examples. Simulation studies suggest that the proposed procedure ensures more accurate estimates than standard nonlinear least squares approaches when the state (initial and/or boundary) conditions are not known. [less ▲]

Detailed reference viewed: 19 (6 ULiège)
Full Text
Peer Reviewed
See detailBayesian inference in an extended SEIR model with nonparametric disease transmission rate: An application to the Ebola epidemic in Sierra Leone
Frasso, Gianluca ULiege; Lambert, Philippe ULiege

in Biostatistics (2016), 17(4), 779-792

Summary The 2014 Ebola outbreak in Sierra Leone is analyzed using a susceptible-exposed-infectious-removed (SEIR) epidemic compartmental model. The discrete time-stochastic model for the epidemic ... [more ▼]

Summary The 2014 Ebola outbreak in Sierra Leone is analyzed using a susceptible-exposed-infectious-removed (SEIR) epidemic compartmental model. The discrete time-stochastic model for the epidemic evolution is coupled to a set of ordinary differential equations describing the dynamics of the expected proportions of subjects in each epidemic state. The unknown parameters are estimated in a Bayesian framework by combining data on the number of new (laboratory confirmed) Ebola cases reported by the Ministry of Health and prior distributions for the transition rates elicited using information collected by the WHO during the follow-up of specific Ebola cases. The time-varying disease transmission rate is modeled in a flexible way using penalized B-splines. Our framework represents a valuable stochastic tool for the study of an epidemic dynamic even when only irregularly observed and possibly aggregated data are available. Simulations and the analysis of the 2014 Sierra Leone Ebola data highlight the merits of the proposed methodology. In particular, the flexible modeling of the disease transmission rate makes the estimation of the effective reproduction number robust to the misspecification of the initial epidemic states and to underreporting of the infectious cases. [less ▲]

Detailed reference viewed: 67 (1 ULiège)
Full Text
Peer Reviewed
See detailParsimonious time series clustering using P-splines
Iorio, Carmela; Frasso, Gianluca ULiege; D’Ambrosio, Antonio et al

in Expert Systems with Applications (2016)

Abstract We introduce a parsimonious model-based framework for clustering time course data. In these applications the computational burden becomes often an issue due to the large number of available ... [more ▼]

Abstract We introduce a parsimonious model-based framework for clustering time course data. In these applications the computational burden becomes often an issue due to the large number of available observations. The measured time series can also be very noisy and sparse and an appropriate model describing them can be hard to define. We propose to model the observed measurements by using P-spline smoothers and then to cluster the functional objects as summarized by the optimal spline coefficients. According to the characteristics of the observed measurements, our proposal can be combined with any suitable clustering method. In this paper we provide applications based on non-hierarchical clustering algorithms. We evaluate the accuracy and the efficiency of our proposal by simulations and by analyzing two real data examples. [less ▲]

Detailed reference viewed: 27 (2 ULiège)
Full Text
Peer Reviewed
See detailParameter estimation and inference in dynamic systems described by linear partial differential equations
Frasso, Gianluca ULiege; Jeager, Jonathan; Lambert, Philippe ULiege

in AStA Advances in Statistical Analysis (2015)

Differential equations (DEs) are commonly used to describe dynamic sys- tems evolving in one (ordinary differential equations or ODEs) or in more than one dimensions (partial differential equations or ... [more ▼]

Differential equations (DEs) are commonly used to describe dynamic sys- tems evolving in one (ordinary differential equations or ODEs) or in more than one dimensions (partial differential equations or PDEs). In real data applications, the para- meters involved in the DE models are usually unknown and need to be estimated from the available measurements together with the state function. In this paper, we present frequentist and Bayesian approaches for the joint estimation of the parameters and of the state functions involved in linear PDEs. We also propose two strategies to include state (initial and/or boundary) conditions in the estimation procedure. We evaluate the performances of the proposed strategy through simulated examples and a real data analysis involving (known and necessary) state conditions. [less ▲]

Detailed reference viewed: 65 (18 ULiège)
Full Text
Peer Reviewed
See detailA Bayesian model for the Ebola epidemic in Sierra Leone
Frasso, Gianluca ULiege; Lambert, Philippe ULiege; Bonou, Wilfried ULiege

in Friedl, Herwig; Wagner, Helga (Eds.) 30th International Workshop on Statistical Modelling, Linz, Austria, 2015, Proceedings (2015, July)

We propose a Bayesian model for the analysis of the 2014 ebola out- break in Sierra Leone. It is based on an extension of the popular compartmental SEIR model speci ed using a system of di erential ... [more ▼]

We propose a Bayesian model for the analysis of the 2014 ebola out- break in Sierra Leone. It is based on an extension of the popular compartmental SEIR model speci ed using a system of di erential equations. [less ▲]

Detailed reference viewed: 42 (5 ULiège)
Full Text
See detailBayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone
Frasso, Gianluca ULiege; Lambert, Philippe ULiege

E-print/Working paper (2015)

The 2014 Ebola outbreak in Sierra Leone is analyzed using an extension of the SEIR compartmental model. The unknown parameters of the system of differential equations are estimated by combining data on ... [more ▼]

The 2014 Ebola outbreak in Sierra Leone is analyzed using an extension of the SEIR compartmental model. The unknown parameters of the system of differential equations are estimated by combining data on the number of new (laboratory confirmed) Ebola cases reported by the Ministry of Health and prior distributions for the transition rates elicited using information collected by the WHO Response Team (2014) during the follow-up of specific Ebola cases. The evolution over time of the disease transmission rate is modeled nonparametrically using penalized B-splines. Our framework represents a valuable and robust stochastic tool for the study of an epidemic dynamic from irregular and possibly aggregated case data. Simulations and the analysis of the 2014 Sierra Leone Ebola data highlight the merits of the proposed methodology. [less ▲]

Detailed reference viewed: 139 (39 ULiège)
Full Text
Peer Reviewed
See detailL- and V-curves for Optimal Smoothing
Frasso, Gianluca ULiege; Eilers, Paul H. C.

in Statistical Modelling (2015), 15(1), 91-111

The L-curve is a tool for the selection of the regularization parameter in ill-posed inverse problems. It is a parametric plot of the size of the residuals vs that of the penalty. The corner of the L ... [more ▼]

The L-curve is a tool for the selection of the regularization parameter in ill-posed inverse problems. It is a parametric plot of the size of the residuals vs that of the penalty. The corner of the L indicates the right amount of regularization. In the context of smoothing the L-curve is easy to compute and works surprisingly well, even for data with correlated noise. We present the theoretical background and applications to real data together with an alternative criterion for finding the corner automatically. We introduce as simplification, the V-curve, which replaces finding the corner of the L-curve by locating a minimum. [less ▲]

Detailed reference viewed: 130 (31 ULiège)
Full Text
Peer Reviewed
See detailModelling the potential of focal screening and treatment as elimination strategy for Plasmodium falciparum malaria in the Peruvian Amazon Region
Rosas-Aguirre, Angel; Erhart, Annette; Llanos-Cuentas, Alejandro et al

in Parasites and Vectors (2015), 8(1), 261

BACKGROUND:Focal screening and treatment (FSAT) of malaria infections has recently been introduced in Peru to overcome the inherent limitations of passive case detection (PCD) and further decrease the ... [more ▼]

BACKGROUND:Focal screening and treatment (FSAT) of malaria infections has recently been introduced in Peru to overcome the inherent limitations of passive case detection (PCD) and further decrease the malaria burden. Here, we used a relatively straightforward mathematical model to assess the potential of FSAT as elimination strategy for Plasmodium falciparum malaria in the Peruvian Amazon Region.METHODS:A baseline model was developed to simulate a scenario with seasonal malaria transmission and the effect of PCD and treatment of symptomatic infections on the P. falciparum malaria transmission in a low endemic area of the Peruvian Amazon. The model was then adjusted to simulate intervention scenarios for predicting the long term additional impact of FSAT on P. falciparum malaria prevalence and incidence. Model parameterization was done using data from a cohort study in a rural Amazonian community as well as published transmission parameters from previous studies in similar areas. The effect of FSAT timing and frequency, using either microscopy or a supposed field PCR, was assessed on both predicted incidence and prevalence rates.RESULTS:The intervention model indicated that the addition of FSAT to PCD significantly reduced the predicted P. falciparum incidence and prevalence. The strongest reduction was observed when three consecutive FSAT were implemented at the beginning of the low transmission season, and if malaria diagnosis was done with PCR. Repeated interventions for consecutive years (10 years with microscopy or 5 years with PCR), would allow reaching near to zero incidence and prevalence rates.CONCLUSIONS:The addition of FSAT interventions to PCD may enable to reach P. falciparum elimination levels in low endemic areas of the Amazon Region, yet the progression rates to those levels may vary substantially according to the operational criteria used for the intervention. [less ▲]

Detailed reference viewed: 16 (2 ULiège)
Full Text
See detailL-surface and V-valley for optimal anisotropic 2D smoothing
Frasso, Gianluca ULiege; Paul, Eilers

E-print/Working paper (2014)

We present the L-surface as an attractive generalization of the L-curve framework for the selection of the optimal smoothing parameters in two dimensional applications. It preserves the desirable features ... [more ▼]

We present the L-surface as an attractive generalization of the L-curve framework for the selection of the optimal smoothing parameters in two dimensional applications. It preserves the desirable features of its unidimensional analogous. The optimal amount of smoothing is indicated by the pair of parameters located in the point of maximum (Gaussian) curvature. Locate this point on a discrete parametric surface can be not straightforward. We introduce the V-valley as a simplified selection criterion based on distance minimization. [less ▲]

Detailed reference viewed: 44 (10 ULiège)
Full Text
See detailAnalysis of MRI stomach scans using differential equations
Frasso, Gianluca ULiege; Lambert, Philippe ULiege; Eilers, Paul H.C.

Poster (2014, September)

Differential equations (DE) are commonly used to describe dynamic systems evolving in one (e.g. time) or more dimensions (e.g. space and time). In real applications the parameters defining the theoretical ... [more ▼]

Differential equations (DE) are commonly used to describe dynamic systems evolving in one (e.g. time) or more dimensions (e.g. space and time). In real applications the parameters defining the theoretical model describing the phenomenon under consideration are often unknown and need to be estimated from the available measurements. This estimation task has been extensively discussed in the statistical literature and probably the most popular procedures are those relying on nonlinear least squares (Bielger et al, 1986). These approaches are computationally intensive and often poorly suited for statistical inference. An attractive alternative is represented by the penalized smoothing procedure introduced by Ramsay et al. (2007). This approach can be viewed as a generalization of the L-spline framework (see Welham et al., 2006 among others) where the flexibility of a high dimensional B-spline expansion of the state function is counterbalanced by a penalty term defining the (set of) differential equation(s) synthesizing the dynamics under investigation. The fidelity of the extracted signal to the hypothesized model is then tuned by a “DE-compliance” parameter to be extracted form the data too. This approach works reasonably well both with linear and nonlinear differential models but, in the latter case, due to the implicit link between the vector of unknown DE parameters and the spline coefficients, the computational burden tends to increase and the optimization of the compliance parameter can be demanding. To overcome these drawbacks we adopt the quasilinearized (QL) ODE-P-spline approach proposed by Frasso et al. (2014). The quasilinearization (Bellman and Kalaba, 1965) step greatly reduces the computational cost of the estimation procedure making the penalty term a second order polynomial function of the DE parameters. As motivating example we present the results of a QL-ODE-P-spline analysis of a set of MRI scans describing stomach contractions during digestion. For illustrative purposes we analyze the measurements related to a single slice of the stomach. Finally, we conclude with a discussion of the possible further extensions of the presented methodology. [less ▲]

Detailed reference viewed: 29 (5 ULiège)
Full Text
Peer Reviewed
See detailComposite smooth estimation of the state price density implied in option prices
Frasso, Gianluca ULiege

in Kneib, Thomas; Sobotka, Fabian; Fahrenholz, Jan (Eds.) et al 29th International Workshop on Statistical Modelling, Göttingen, Germany, 2014, Proceedings (2014, July)

We propose a new semi-parametric approach for the estimation of the State Price Density (SPD) implied in option prices. Our procedure is inspired by a Penalized Composite Link Model (PCLM) approach and ... [more ▼]

We propose a new semi-parametric approach for the estimation of the State Price Density (SPD) implied in option prices. Our procedure is inspired by a Penalized Composite Link Model (PCLM) approach and ensures smooth and arbitrage-free estimates. [less ▲]

Detailed reference viewed: 16 (0 ULiège)
Full Text
See detailEstimation and approximation in nonlinear dynamic systems using quasilinearization
Frasso, Gianluca ULiege; Jaeger, Jonathan ULiege; Lambert, Philippe ULiege

E-print/Working paper (2014)

Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems. In this paper we propose a smoothing approach regularized by a ... [more ▼]

Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems. In this paper we propose a smoothing approach regularized by a quasilinearized ODE-based penalty in order to approximate the state functions and estimate the parameters defining nonlinear differential systems from noisy data. Within the quasilinearized spline based framework, the estimation process reduces to a conditionally linear problem for the optimization of the spline coefficients. Furthermore, standard ODE compliance parameter(s) selection criteria are easily applicable and conditions on the state function(s) can be eventually imposed using soft or hard constraints. The approach is illustrated on real and simulated data. [less ▲]

Detailed reference viewed: 25 (6 ULiège)
See detailModel-free probability distance clustering of time series
Frasso, Gianluca ULiege; D'Ambrosio, Antonio; Siciliano, Roberta

Conference (2013, December)

Detailed reference viewed: 11 (0 ULiège)
Full Text
See detailEstimation and approximation in multidimensional dynamics
Frasso, Gianluca ULiege; Jaeger, Jonathan ULiege; Lambert, Philippe ULiege

E-print/Working paper (2013)

Differential equations (DEs) are commonly used to describe dynamic systems evolving in one (ordinary differential equations or ODEs) or in more than one dimensions (partial differential equations or PDEs ... [more ▼]

Differential equations (DEs) are commonly used to describe dynamic systems evolving in one (ordinary differential equations or ODEs) or in more than one dimensions (partial differential equations or PDEs). In real data applications the parameters involved in the DE models are usually unknown and need to be estimated from the available measurements together with the state function. In this paper, we present frequentist and Bayesian approaches for the joint estimation of the parameters and of the state functions involved in PDEs. We also propose two strategies to include differential (initial and/or boundary) conditions in the estimation procedure. We evaluate the performances of the proposed strategy on simulated and real data applications. [less ▲]

Detailed reference viewed: 56 (13 ULiège)