Search results for "parametri"
showing 10 items of 1144 documents
Forecasting Exchange Rates Volatilities Using Artificial Neural Networks
2000
This paper employs Artificial Neural Networks to forecast volatilities of the exchange rates of six currencies against the Spanish peseta. First, we propose to use ANN as an alternative to parametric volatility models, then, we employ them as an aggregation procedure to build hybrid models. Though we do not find a systematic superiority of ANN, our results suggest that they are an interesting alternative to classical parametric volatility models.
On the effects of the Double-Walled Tubes lay-out on the DEMO WCLL breeding blanket module thermal behavior
2019
Abstract The EU-DEMO Water-Cooled Lithium Lead Breeding Blanket (WCLL BB) concept foresees liquid Pb-15.7Li eutectic alloy as breeder and neutron multiplier, whereas pressurized subcooled water as coolant, with operative conditions typical of the PWR fission reactors (temperature in the range of 295–328 °C and pressure of 15.5 MPa). The cooling down of the BB is guaranteed by means of two separated cooling circuits: the one consisted in square channels housed within the complex of Side Walls and First Wall, and the one composed of a set of Double-Walled Tubes (DWTs) submerged in the Breeding Zone (BZ) and deputed to remove heat power therein generated. A parametric thermal study has been ca…
Parametric study of the influence of double-walled tubes layout on the DEMO WCLL breeding blanket thermal performances
2020
Abstract Within the framework of the EUROfusion activities regarding the EU-DEMO Breeding Blanket (BB) concept, the University of Palermo is long-time involved, in close cooperation with ENEA, in the design of the Water Cooled Lithium Lead (WCLL) BB, that is one of the two concepts under consideration for the DEMO reactor. It is mainly characterized by a liquid lithium-lead eutectic alloy acting as breeder and neutron multiplier, as well as by subcooled pressurized water flowing as coolant under PWR-like conditions (pressure of 15.5 MPa and inlet/outlet temperatures of 295 °C/328 °C). A research campaign has been recently carried out to study the potential influence of the Breeding Zone coo…
Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis
2016
Mechanisms of human color vision are characterized by two phenomenological aspects: the system is nonlinear and adaptive to changing environments. Conventional attempts to derive these features from statistics use separate arguments for each aspect. The few statistical explanations that do consider both phenomena simultaneously follow parametric formulations based on empirical models. Therefore, it may be argued that the behavior does not come directly from the color statistics but from the convenient functional form adopted. In addition, many times the whole statistical analysis is based on simplified databases that disregard relevant physical effects in the input signal, as, for instance…
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
2018
This letter introduces warped Gaussian process (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such a prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more a…
Critical comments on EEG sensor space dynamical connectivity analysis
2019
Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations canno…
Modeling temporal treatment effects with zero inflated semi-parametric regression models: The case of local development policies in France
2017
International audience; A semi-parametric approach is proposed to estimate the variation along time of the effects of two distinct public policies that were devoted to boost rural development in France over a similar period of time. At a micro data level, it is often observed that the dependent variable, such as local employment, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a continuous response model. We introduce a conditional mixture model which combines a mass at zero and a continuous response. The suggested zero inflated semi-parametric statistical approach relies on the flexibility and modularity of additive models with the abi…
Bayesian classification for dating archaeological sites via projectile points
2021
Dating is a key element for archaeologists. We propose a Bayesian approach to provide chronology to sites that have neither radiocarbon dating nor clear stratigraphy and whose only information comes from lithic arrowheads. This classifier is based on the Dirichlet-multinomial inferential process and posterior predictive distributions. The procedure is applied to predict the period of a set of undated sites located in the east of the Iberian Peninsula during the IVth and IIIrd millennium cal. BC.
Joint Gaussian Processes for Biophysical Parameter Retrieval
2017
Solving inverse problems is central to geosciences and remote sensing. Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem, and for this reason, often the application of a nonlinear statistical regression is preferred. In general, regression models predict the biophysical parameter of interest from the corresponding received radiance. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, co…
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
2021
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and…