E. Bukina

Bio: E. Bukina is an academic researcher from University of Nice Sophia Antipolis. The author has contributed to research in topics: Epistemology & Natural law. The author has an hindex of 1, co-authored 2 publications receiving 6 citations.

Fredholm integral relation between compound estimation and prediction (FIRCEP)

Abstract: We discuss the following problem: Given a set of information criteria for optimal designs, the numerical and computational complexity may drastically differ from one criterion to another. A general...

Introduction to integral equations with applications, by Abdul J. Jerri. Pp 254. $47·50. 1985. ISBN 0-8247-7293-8 (Marcel Dekker)

Abstract: Integral Equations, Origin, and Basic Tools Modeling of Problems as Integral Equations Volterra Integral Equations The Green's Function Fredholm Integral Equations Existence of the Solutions: Basic Fixed Point Theorems Higher Quadrature Rules for the Numerical Solutions Appendices Answers to Exercises References Index.

On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

Abstract: We formulate ill-posedness of inverse problems of estimation and prediction of Coronavirus Disease 2019 (COVID-19) outbreaks from statistical and mathematical perspectives. This is by nature a stoc...

A novel adaptive control design method for stochastic nonlinear systems using neural network

Abstract: This paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

Latent variables and space-time models for environmental problems

Abstract: During the conference of the Italian Statistical Society on ‘‘Advances in Latent Variables’’ held in Brescia, Italy, June 19–21, 2013, the working group on environmental statistics, named GRASPA (http://www.graspa.org), organized the special track on ‘‘Space and space-time models: methods and environmental applications’’. This scientific activity was intended to celebrate the new condition as a permanent group of the Italian statistical society and renew GRASPA’s long life, which dates back to 1989. The idea of a special issue on the track main theme arose considering not only the number and scientific quality of the track contributions, but also the general interest of the topic, which exceeds the conference coverage. The result, raising from both track attendance and space-time statistical scientific community, is an interesting blend of various topics, which amounts at fourteen papers covering various environmental problems. To a large extent climate problems are considered, including upper atmosphere monitoring, rain simulation, air and water temperature and sea currents. Moreover, applications considered water body ecology, seismology and radon emissions. From the methodological point of view, areas covered by the special issue are scalar and vector-valued stochastic processes with continuous index over space or over spacetime. Moreover, functional data, point processes and time series methods are taken into account. Latent variables enter in a natural way in many of these papers either as spatial or temporal components. This methodological key is loosely used in the rest of this editorial to briefly review the special issue contributions. As for spatial processes methods, the work by RuizMedina and Porcu (2014) puts the theoretical basis for understanding the equivalence of Gaussian measures that index multivariate Gaussian fields. Their work will open the lines for research in estimation of Gaussian fields through tapering techniques as well as for assessing the properties of estimators of spatial dependence under infill asymptotics. For binary spatial data, which are built on the basis of a truncated latent Gaussian model, Bevilacqua et al. (2014) explore the possibility of building Euclidean likelihoods, obtaining high computational benefits and ensuring a reasonable level of statistical efficiency. In the same framework of spatial data, Verdin et al. (2014) introduce a stochastic weather generator for the variables of minimum and maximum temperature, as well as precipitation occurrence. In particular, temperature variables are modeled in a vector autoregressive framework, conditional on precipitation occurrence, whilst this last arises via a probit model. Both temperature and occurrence are spatially correlated using spatial Gaussian processes. Fontanella et al. (2014) consider a generalized latent-spatialquantile regression (GLSQR) model for the understanding of indoor radon gas monitoring in central Italy. Vallejo et al. (2014) consider a method for image landscape classification based on the assumption that the vector of image bands is a spatial multivariate process. They build such classification using the divergence of a modified Mahalanobis distance, given by the codispersion matrix. Methods for space-time processes are faced with great detail in this issue. If spatial design represents a critical issue, the paper by Stehlik et al. (2014) gives a clear picture A. Fasso (&) University of Bergamo, Viale Marconi 5, 24044 Dalmine BG, Italy e-mail: alessandro.fasso@unibg.it