Bayesian modeling of financial returns: A relationship between volatility and trading volume

The modified mixture model with Markov switching volatility specification is introduced to analyze the relationship between stock return volatility and trading volume. We propose to construct an algorithm based on Markov chain Monte Carlo simulation methods to estimate all the parameters in the model using a Bayesian approach. The series of returns and trading volume of the British Petroleum stock will be analyzed. Copyright © 2009 John Wiley & Sons, Ltd.     

A Note on the Finite Variance of the Averaging Function for Polynomial System Solving

In the forthcoming paper of Beltrán and Pardo, the average complexity of linear homotopy methods to solve polynomial equations with random initial input (in a sense to be described below) was proven to be finite, and even polynomial in the size of the input. In this paper, we prove that some other higher moments are also finite. In particular, we show that the variance is polynomial in the size of the input.     

Stabilization of stochastic systems under Markovian switching

The problem of state feedback stabilization of discrete-time stochastic processes under Markovian switching is considered. The jump Markovian switching is modeled by a discrete-time Markov chain, and the noise or stochastic environmental disturbance is modeled by a sequence of identically independently normally distributed random variables. Necessary and sufficient conditions based on linear matrix inequalities (LMI’s) for stochastic stability is obtained. The proposed control law for this stochastic stabilization result depends on the mode of the system as well as the environmental disturbances. The robustness results of such stability concepts against all admissible uncertainties are also investigated. An example is given to demonstrate the obtained results.     

Soil δ15N patterns in old-growth forests of southern Chile as integrator for N-cycling

Old-growth forests of southern Chile represent an important reserve of temperate (rain) forests in the world. Wetter and colder forest ecosystems appear to be more efficient in conserving and recycling N such that mostly non-plant available N species are lost, which could be indicated by more depleted δ¹⁵N values of the soil and plants. Hydrological N loss from the old-growth forests in southern Chile occurs mainly via dissolved organic nitrogen and not via dissolved inorganic N. Forest disturbances (e.g. fire, clear-cutting or enhanced N deposition) cause (abrupt) changes in ecosystem N-cycling processes. In this study, we hypothesized that δ¹⁵N signatures of soil profiles under old-growth forests could be used as an integrator for ecosystem N-cycling, and changes of these δ¹⁵N profiles could be valuable to assess ecosystem resilience towards disturbances. Six old-growth forests were selected in the phytogeographical region of the Valdivian rain forest in southern Chile. One of the sites has been partly burned in February 2002. First, we observed that ecosystems with higher mean annual precipitation and lower mean annual temperature were relatively more depleted in ¹⁵N. Secondly, we found that a forest fire caused a 100-fold increase of the nitrate export and induced an enrichment of the soil δ¹⁵N signal in the upper 20 cm.     

Wood identification using pressure DSC data

This work addresses the problem of supervised classification of industrial wood species (seven different types in the present study) through their thermo‐oxidative stability. This is evaluated by pressure differential scanning calorimetry (PDSC) using the ASTM E2009. The maximization of the ratio of correct classification and the reduction of the costs of this activity are intended. This supervised classification problem was carried out using two different proposals: applying novel nonparametric functional data analysis techniques, based on kernel estimation, to the original PDSC curves, and using machine learning classification approaches applied to different multivariate data sets. The multivariate data sets were obtained, on the one hand, by estimating the fractal (Hausdorff) dimension of the PDSC curves by several methods, jointly with selecting the parameters from fitting a nonlinear model to the PDSC curves and, on the other hand, applying principal component analysis or partial linear squares to the thermograms. The results obtained show that the PDSC curves can be used to discriminate wood samples when these innovative and traditional statistical techniques are applied. In the best of the cases, a probability of correct classification that equals to 0.92 was obtained. PDSC represents a new alternative to the use of images, spectra, and other thermal signals as thermogravimetric analysis for classification purposes.Copyright © 2013 John Wiley & Sons, Ltd.     

Optical Diagnosis for Thyroid and Parathyroid Tissues—A Review

Abstract

This article presents an overview of optical methodologies to aid the diagnosis and differentiation of thyroid and parathyroid tissues. In particular, we review the several techniques and associated methodologies that allow in vivo and ex vivo optical characterization of thyroid and parathyroid tissues. Emphasis is placed on the research potential of these techniques and whether intrinsic characteristics can provide useful contrast for the diagnosis of human thyroid and parathyroid malignant lesions.