Copula-based regression models: A survey

In this review paper we collect several results about copula-based models, especially concerning regression models, by focusing on some insurance applications. (C) 2009 Elsevier B.V. All rights reserved.

The land-atmosphere water flux in the tropics

Tropical vegetation is a major source of global land surface evapotranspiration, and can thus play a major role in global hydrological cycles and global atmospheric circulation. Accurate prediction of tropical evapotranspiration is critical to our understanding of these processes under changing climate. We examined the controls on evapotranspiration in tropical vegetation at 21 pan-tropical eddy covariance sites, conducted a comprehensive and systematic evaluation of 13 evapotranspiration models at these sites, and assessed the ability to scale up model estimates of evapotranspiration for the test region of Amazonia. Net radiation was the strongest determinant of evapotranspiration (mean evaporative fraction was 0.72) and explained 87% of the variance in monthly evapotranspiration across the sites. Vapor pressure deficit was the strongest residual predictor (14%), followed by normalized difference vegetation index (9%), precipitation (6%) and wind speed (4%). The radiation-based evapotranspiration models performed best overall for three reasons: (1) the vegetation was largely decoupled from atmospheric turbulent transfer (calculated from X decoupling factor), especially at the wetter sites; (2) the resistance-based models were hindered by difficulty in consistently characterizing canopy (and stomatal) resistance in the highly diverse vegetation; (3) the temperature-based models inadequately captured the variability in tropical evapotranspiration. We evaluated the potential to predict regional evapotranspiration for one test region: Amazonia. We estimated an Amazonia-wide evapotranspiration of 1370 mm yr(-1), but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096 mm yr(-1)) is considered in discussion on the use of flux data to validate and interpolate models.

Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Interaction of oligonucleotide-based amphiphilic block copolymers with cell membrane models

Oligonucleotides have unique molecular recognition properties, being involved in biological mechanisms such as cell-surface receptor recognition or gene silencing. For their use in human therapy for drug or gene delivery, the cell membrane remains a barrier, but this can be obviated by grafting a hydrophobic tail to the oligonucleotide. Here we demonstrate that two oligonucleotides, one consisting of 12 guanosine units (G(12)), and the other one consisting of five adenosine and seven guanosine (A(5)G(7)) units, when functionalized with poly(butadiene), namely PB-G(12) and PB-A(5)G(7), can be inserted into Langmuir monolayers of dipalmitoyl phosphatidyl choline (DPPC), which served as a cell membrane model. PB-G(12) and PB-A(5)G(7) were found to affect the DPPC monolayer even at high surface pressures. The effects from PB-G(12) were consistently stronger, particularly in reducing the elasticity of the DPPC monolayers, which may have important biological implications. Multilayers of DPPC and nucleotide-based copolymers could be adsorbed onto solid supports, in the form of Y-type LB films, in which the molecular-level interaction led to lower energies in the vibrational spectra of the nucleotide-based copolymers. This successful deposition of solid films opens the way for devices to be produced which exploit the molecular recognition properties of the nucleotides. (C) 2010 Elsevier Inc. All rights reserved.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.