Search results for "Parametric"

Nonparametric estimation of quantile versions of the Lorenz curve

2018

Non-parametric probabilistic forecasting of academic performance in Spanish high school using an epidemiological modelling approach

2013

Academic underachievement is a concern of paramount importance in Europe, and particularly in Spain, where around of 30% of the students in the last two courses in high school do not achieve the minimum knowledge academic requirement. In order to analyse this problem, we propose a mathematical model via a system of ordinary differential equations to study the dynamics of the academic performance in Spain. Our approach is based on the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. Moreover, in order to consider the uncertainty in the estimation of model parameters, a bootstrapping approach is employed. This technique permits to for…

Bayesian two-stage regression with parametric heteroscedasticity

2008

In this paper, we expand Kleibergen and Zivot's (2003) Bayesian two-stage (B2S) model by allowing for unequal variances. Our choice for modeling heteroscedasticity is a fully Bayesian parametric approach. As an application, we present a cross-country Cobb–Douglas production function estimation.

Estimation of ordered response models with sample selection

2011

We introduce two new Stata commands for the estimation of an ordered response model with sample selection. The opsel command uses a standard maximum-likelihood approach to fit a parametric specification of the model where errors are assumed to follow a bivariate Gaussian distribution. The snpopsel command uses the semi-nonparametric approach of Gallant and Nychka (1987, Econometrica 55: 363–390) to fit a semiparametric specification of the model where the bivariate density function of the errors is approximated by a Hermite polynomial expansion. The snpopsel command extends the set of Stata routines for semi-nonparametric estimation of discrete response models. Compared to the other semi-n…

B-Spline Estimation in a Survey Sampling Framework

2021

Nonparametric regression models have been used more and more over the last years to model survey data and incorporate efficiently auxiliary information in order to improve the estimation of totals, means or other study parameters such as Gini index or poverty rate. B-spline nonparametric regression has the benefit of being very flexible in modeling nonlinear survey data while keeping many similarities and properties of the classical linear regression. This method proved to be efficient for deriving a unique system of weights which allowed to estimate in an efficient way and simultaneously many study parameters. Applications on real and simulated survey data showed its high efficiency. This …

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…