Search results for "EDAS"

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

2020

Abstract Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-base…

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…

Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation

2013

Although stock prices fluctuate, the variations are relatively small and are frequently assumed to be normally distributed on a large time scale. But sometimes these fluctuations can become determinant, especially when unforeseen large drops in asset prices are observed that could result in huge losses or even in market crashes. The evidence shows that these events happen far more often than would be expected under the generalised assumption of normally distributed financial returns. Thus it is crucial to model distribution tails properly so as to be able to predict the frequency and magnitude of extreme stock price returns. In this paper we follow the approach suggested by McNeil and Frey …

Stock Return Volatility on Scandinavian Stock Markets and the Banking Industry: Evidence from the Years of Financial Liberalisation and Banking Crisis

1999

This paper investigates the evolution of the (conditional) volatility of returns on three Scandinavian markets (Finland, Norway and Sweden) over the turbulent period of the past decade, namely the overlapping periods of financial liberalisation, drastically changing macroeconomic conditions and banking crisis. We find that even over this relatively turbulent period volatility is in most cases successfully captured by past volatility and shocks to past volatility, ie by a (symmetric) GARCH process. In each country banking crisis has induced regime shifts in (unconditional) volatility. We also find evidence for cross-country volatility spillovers during the banking crisis episodes. The estima…

Suomen, Saksan ja Ranskan joukkovelkakirjojen korkoerojen kehitys EMU-vaiheiden aikana

2001

The Nexus between Sovereign CDS and Stock Market Volatility: New Evidence

2021

This paper extends the studies published to date by performing an analysis of the causal relationships between sovereign CDS spreads and the estimated conditional volatility of stock indices. This estimation is performed using a vector autoregressive model (VAR) and dynamically applying the Granger causality test. The conditional volatility of the stock market has been obtained through various univariate GARCH models. This methodology allows us to study the information transmissions, both unidirectional and bidirectional, that occur between CDS spreads and stock volatility between 2004 and 2020. We conclude that CDS spread returns cause (in the Granger sense) conditional stock volatility, m…

Empirical Likelihood-Based ANOVA for Trimmed Means

2016

In this paper, we introduce an alternative to Yuen’s test for the comparison of several population trimmed means. This nonparametric ANOVA type test is based on the empirical likelihood (EL) approach and extends the results for one population trimmed mean from Qin and Tsao (2002). The results of our simulation study indicate that for skewed distributions, with and without variance heterogeneity, Yuen’s test performs better than the new EL ANOVA test for trimmed means with respect to control over the probability of a type I error. This finding is in contrast with our simulation results for the comparison of means, where the EL ANOVA test for means performs better than Welch’s heteroscedastic…

GARCH models with changes in variance: An approximation to risk measurements

2003

This study aims to model volatility as an approximation to an optimum measurement of stock market risk because of the importance of this concept for, among other things, the proper management of portfolios. Following the proposal of Lamoureux and Lastrapes (1990), the authors consider that the high degree of persistence detected in GARCH models arises from a poor specification of the equation of the variance due to not considering the possible deterministic changes in the unconditional variance of the financial series. To determine the point in time as well as the duration of these changes, the proposal made by Inclan and Tiao (1994) is used. As an empirical application, whether or not the …

Biophysical parameter retrieval with warped Gaussian processes

2015

This paper focuses on biophysical parameter retrieval based on Gaussian Processes (GPs). Very often an arbitrary transformation is applied to the observed variable (e.g. chlorophyll content) to better pose the problem. This standard practice essentially tries to linearize/uniformize the distribution by applying non-linear link functions like the logarithmic, the exponential or the logistic functions. In this paper, we propose to use a GP model that automatically learns the optimal transformation directly from the data. The so-called warped GP regression (WGPR) presented in [1] models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are…

Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – A comparison

2015

Abstract Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the …