References of "Lambert, Philippe"
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See detailLaplace approximations for fast Bayesian inference in generalized additive models based on P-splines
Gressani, Oswaldo; Lambert, Philippe ULiege

in Computational Statistics and Data Analysis (in press)

Generalized additive models (GAMs) are a well-established statistical tool for modeling complex nonlinear relationships between covariates and a response assumed to have a conditional distribution in the ... [more ▼]

Generalized additive models (GAMs) are a well-established statistical tool for modeling complex nonlinear relationships between covariates and a response assumed to have a conditional distribution in the exponential family. To make inference in this model class, a fast and flexible approach is considered based on Bayesian P-splines and the Laplace approximation. The proposed Laplace-P-spline model contributes to the development of a new methodology to explore the posterior penalty space by considering a deterministic grid-based strategy or a Markov chain sampler, depending on the number of smooth additive terms in the predictor. The approach has the merit of relying on a simple Gaussian approximation to the conditional posterior of latent variables with closed form analytical expressions available for the gradient and Hessian of the approximate posterior penalty vector. This enables to construct accurate posterior pointwise and credible set estimators for (functions of) regression and spline parameters at a relatively low computational budget even for a large number of smooth additive components. The performance of the Laplace-P-spline model is confirmed through different simulation scenarios and the method is illustrated on two real datasets. [less ▲]

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See detailPick up and release of micro-objects: A motion-free method to change the conformity of a capillary contact
Iazzolino, Antonio ULiege; Tourtit, Youness ULiege; Chafaï, A. et al

in Soft Matter (2020), 16(3), 754-763

We propose a new 3D-printed capillary gripper equipped with a textured surface for motion-free release. The gripper classically picks up micro-objects thanks to the capillary forces induced by a liquid ... [more ▼]

We propose a new 3D-printed capillary gripper equipped with a textured surface for motion-free release. The gripper classically picks up micro-objects thanks to the capillary forces induced by a liquid bridge. Micro-objects are released by decreasing the volume of this bridge through evaporation. The latter can be either natural or speeded up by a heating source (an IR laser or the Joule effect). The volume reduction changes the conformity of the contact between the gripper and the object. We analyze the gripper performance and the capillary force generated, and then we rationalize the release mechanism by defining the concept of contact conformity in the context of capillary forces. © 2020 The Royal Society of Chemistry. [less ▲]

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See detailInclusion of time-varying covariates in cure survival models with an application in fertility studies
Lambert, Philippe ULiege; Bremhorst, Vincent

in Journal of the Royal Statistical Society. Series A: Statistics in Society (2020), 183

Cure survival models are used when we desire to acknowledge explicitly that an unknown proportion of the population studied will never experience the event of interest. An extension of the promotion time ... [more ▼]

Cure survival models are used when we desire to acknowledge explicitly that an unknown proportion of the population studied will never experience the event of interest. An extension of the promotion time cure model enabling the inclusion of time-varying covariates as regressors when modelling (simultaneously) the probability and the timing of the monitored event is presented. Our proposal enables us to handle non-monotone population hazard functions without a specific parametric assumption on the baseline hazard. This extension is motivated by and illustrated on data from the German Socio-Economic Panel by studying the transition to second and third births in West Germany. [less ▲]

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See detailEstimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival
Lambert, Philippe ULiege; Bremhorst, Vincent

in Biometrical Journal (2019), 61(2), 275-289

The promotion time cure model is a survival model acknowledging that an unidentified proportion of subjects will never experience the event of interest whatever the duration of the follow-up. We focus our ... [more ▼]

The promotion time cure model is a survival model acknowledging that an unidentified proportion of subjects will never experience the event of interest whatever the duration of the follow-up. We focus our interest on the challenges raised by the strong posterior correlation between some of the regression parameters when the same covariates influence long- and short-term survival. Then, the regression parameters of shared covariates are strongly correlated with, in addition, identification issues when the maximum follow-up duration is insufficiently long to identify the cured fraction. We investigate how, despite this, plausible values for these parameters can be obtained in a computationally efficient way. The theoretical properties of our strategy will be investigated by simulation and illustrated on clinical data. Practical recommendations will also be made for the analysis of survival data known to include an unidentified cured fraction. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim [less ▲]

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See detailNonparametric double additive cure survival models: An application to the estimation of the non-linear effect of age at first parenthood on fertility progression
Bremhorst, V.; Kreyenfeld, M.; Lambert, Philippe ULiege

in Statistical Modelling (2019), 19(3), 248-275

This article introduces double additive models to describe the effect of continuous covariates in cure survival models, thereby relaxing the traditional linearity assumption in the two regression parts ... [more ▼]

This article introduces double additive models to describe the effect of continuous covariates in cure survival models, thereby relaxing the traditional linearity assumption in the two regression parts. This class of models extends the classical event history models when an unknown proportion of the population under study will never experience the event of interest. They are used on data from the German Socio-Economic Panel (GSOEP) to examine how age at first birth relates to the timing and quantum of fertility for given education levels of the respondents. It is shown that the conditional probability of having further children decreases with the mother's age at first birth. While the effect of age at first birth in the third birth's probability model is fairly linear, this is not the case for the second child with an accelerating decline detected for women that had their first kid beyond age 30. © 2019, SAGE Publications. [less ▲]

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See detailFast Bayesian inference using Laplace approximations in a flexible promotion time cure model based on P-splines
Gressani, Oswaldo; Lambert, Philippe ULiege

in Computational Statistics and Data Analysis (2018), 124

Bayesian methods for flexible time-to-event models usually rely on the theory of Markov chain Monte Carlo (MCMC) to sample from posterior distributions and perform statistical inference. These techniques ... [more ▼]

Bayesian methods for flexible time-to-event models usually rely on the theory of Markov chain Monte Carlo (MCMC) to sample from posterior distributions and perform statistical inference. These techniques are often plagued by several potential issues such as high posterior correlation between parameters, slow chain convergence and foremost a strong computational cost. A novel methodology is proposed to overcome the inconvenient facets intrinsic to MCMC sampling with the major advantage that posterior distributions of latent variables can rapidly be approximated with a high level of accuracy. This can be achieved by exploiting the synergy between Laplace's method for posterior approximations and P-splines, a flexible tool for nonparametric modeling. The methodology is developed in the class of cure survival models, a useful extension of standard time-to-event models where it is assumed that an unknown proportion of unidentified (cured) units will never experience the monitored event. An attractive feature of this new approach is that point estimators and credible intervals can be straightforwardly constructed even for complex functionals of latent model variables. The properties of the proposed methodology are evaluated using simulations and illustrated on two real datasets. The fast computational speed and accurate results suggest that the combination of P-splines and Laplace approximations can be considered as a serious competitor of MCMC to make inference in semi-parametric models, as illustrated on survival models with a cure fraction. [less ▲]

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See detailFertility progression in Germany: an analysis using flexible nonparametric cure survival models
Bremhorst, Vincent; Kreyenfeld, Michaela; Lambert, Philippe ULiege

in Demographic Research (2016)

OBJECTIVE This paper uses data from the German Socio-Economic Panel (GSOEP) to study the transition to second and third births. In particular, we seek to distinguish the factors that determine the timing ... [more ▼]

OBJECTIVE This paper uses data from the German Socio-Economic Panel (GSOEP) to study the transition to second and third births. In particular, we seek to distinguish the factors that determine the timing of fertility from the factors that influence ultimate parity progression. METHODS We employ cure survival models, a technique commonly used in epidemiological studies and in the statistical literature but only rarely applied to fertility research. RESULTS We find that education has a different impact on the timing and the ultimate probability of having a second and a third birth. Furthermore, we show that the shape of the fertility schedule for the total population differs from that of ‘susceptible women’ (i.e., those who have a second or a third child). CONCLUSION Standard event history models conflate timing and quantum effects. Our approach overcomes this shortcoming. It estimates separate parameters for the hazard rate of having a next child for the ‘susceptible population’ and the ultimate probability of having another child for the entire population at risk. [less ▲]

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See detailSemiparametric frailty model for clustered interval-censored data
Cetinyürek, Aysun ULiege; Lambert, Philippe ULiege

Report (2016)

The shared frailty model is a popular tool to analyze correlated right-censored time-to-event data. In the shared frailty model, the latent frailty is assumed to be shared by the members of a cluster and ... [more ▼]

The shared frailty model is a popular tool to analyze correlated right-censored time-to-event data. In the shared frailty model, the latent frailty is assumed to be shared by the members of a cluster and is assigned a parametric distribution, typically a gamma distribution due to its conjugacy. In the case of interval-censored time-to-event data, the inclusion of frailties results in complicated intractable likelihoods. Here, we propose a exible frailty model for analyzing such data by assuming a smooth semiparametric form for the conditional time-to-event distribution and a parametric or a exible form for the frailty distribution. The results of a simulation study suggest that the estimation of regression parameters is robust to misspeci cation of the frailty distribution (even when the frailty distribution is multimodal or skewed). Given su ciently large sample sizes and number of clusters, the exible approach produces smooth and accurate posterior estimates for the baseline survival function and for the frailty density, and can correctly detect and identify unusual frailty density forms. The methodology is illustrated using dental data from the Signal Tandmobiel® Study. [less ▲]

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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 ▲]

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See detailA Bayesian approach to the semiparametric estimation of a minimum inhibitory concentration distribution
Jaspers, Stijn; Lambert, Philippe ULiege; Aerts, Marc

in Annals of Applied Statistics (2016), 10(2), 906-924

Bacteria that have developed a reduced susceptibility against antimicrobials pose a major threat to public health. Hence, monitoring their distribution in the general population is of major importance ... [more ▼]

Bacteria that have developed a reduced susceptibility against antimicrobials pose a major threat to public health. Hence, monitoring their distribution in the general population is of major importance. This monitoring is performed based on minimum inhibitory concentration (MIC) values, which are collected through dilution experiments. We present a semiparametric mixture model to estimate the MIC density on the full continuous scale. The wild-type first component is assumed to be of a parametric form, while the nonwild-type second component is modelled nonparametrically using Bayesian P-splines combined with the composite link model. A Metropolis within Gibbs strategy was used to draw a sample from the joint posterior. The newly developed method was applied to a specific bacterium–antibiotic combination, that is, Escherichia coli tested against ampicillin. After obtaining an estimate for the entire density, model-based classification can be performed to check whether or not an isolate belongs to the wild-type subpopulation. The performance of the new method, compared to two existing competitors, is assessed through a small simulation study. [less ▲]

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See detailSemi-parametric frailty model for clustered interval-censored data
Cetinyürek, Aysun; Lambert, Philippe ULiege

in Statistical Modelling (2016), 16(5), 360-391

The shared frailty model is a popular tool to analyze correlated right-censored time-to-event data. In the shared frailty model, the latent frailty is assumed to be shared by the members of a cluster and ... [more ▼]

The shared frailty model is a popular tool to analyze correlated right-censored time-to-event data. In the shared frailty model, the latent frailty is assumed to be shared by the members of a cluster and is assigned a parametric distribution, typically a gamma distribution due to its conjugacy. In the case of interval-censored time-to-event data, the inclusion of frailties results in complicated intractable likelihoods. Here, we propose a flexible frailty model for analyzing such data by assuming a smooth semi-parametric form for the conditional time-to-event distribution and a parametric or a flexible form for the frailty distribution. The results of a simulation study suggest that the estimation of regression parameters is robust to misspecification of the frailty distribution (even when the frailty distribution is multimodal or skewed). Given sufficiently large sample sizes and number of clusters, the flexible approach produces smooth and accurate posterior estimates for the baseline survival function and for the frailty density, and it can correctly detect and identify unusual frailty density forms. The methodology is illustrated using dental data from the Signal Tandmobiel® study. [less ▲]

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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 ▲]

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See detailFlexible estimation in cure survival models using Bayesian P-splines
Bremhorst, Vincent; Lambert, Philippe ULiege

in Computational Statistics and Data Analysis (2016), 93

In the analysis of survival data, it is usually assumed that any unit will experience the event of interest if it is observed for a sufficiently long time. However, it can be explicitly assumed that an ... [more ▼]

In the analysis of survival data, it is usually assumed that any unit will experience the event of interest if it is observed for a sufficiently long time. However, it can be explicitly assumed that an unknown proportion of the population under study will never experience the monitored event. The promotion time model, which has a biological motivation, is one of the survival models taking this feature into account. The promotion time model assumes that the failure time of each subject is generated by the minimum of N independent latent event times with a common distribution independent of N. An extension which allows the covariates to influence simultane- ously the probability of being cured and the latent distribution is presented. The latent distribution is estimated using a flexible Cox proportional hazard model where the logarithm of the baseline hazard function is specified using Bayesian P-splines. Introducing covariates in the latent distribution implies that the population hazard function might not have a proportional hazard structure. However, the use of P- splines provides a smooth estimation of the population hazard ratio over time. The identification issues of the model are discussed and a restricted use of the model when the follow up of the study is not sufficiently long is proposed. The accuracy of our methodology is evaluated through a simulation study and the model is illustrated on data from a Melanoma clinical trial. [less ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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See detailTesting the proportional odds assumption in multiply imputed ordinal longitudinal data
Donneau, Anne-Françoise ULiege; Mauer, M; Lambert, Philippe ULiege et al

in Journal of Applied Statistics (2015), 42(10), 2257-2279

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See detailSimulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings
Donneau, Anne-Françoise ULiege; Mauer, Murielle; Lambert, Philippe ULiege et al

in Journal of Biopharmaceutical Statistics (2015), 25(03), 570-601

The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI method can be classified into two broad classes, the ... [more ▼]

The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI method can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for the longitudinal ordinal data setting under the missing at random (MAR) assumption is not well documented. This paper intends to fill this gap by conducting a large simulation study on the estimation of the parameters of a longitudinal proportional odds model. The two MI methods are also illustrated in quality of life data from a cancer clinical trial. [less ▲]

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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 ▲]

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