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Prevalence and characterization of Salmonella spp. in three shellfish-harvesting areas in France

Shellfish farming is an important economic activity in the Brittany and Normandy regions. However, a part of the production sites corresponds to relatively sensitive areas where the presence of faecal microorganisms is a major concern for shellfish and constitutes a possible health risk. Indeed, shellfish bioaccumulates in their tissues pathogenic contaminants present in water and can cause food-borne diseases such as salmonellosis. During a two-year study, we evaluated the presence of faecal indicators, measured the prevalence of Salmonella spp., isolated and characterized Salmonella spp. from three French shellfish-harvesting areas (shellfish and sediment) and their watersheds (from river water samples).

Parameter estimation of structural commodity price models

Commodity price modeling is normally approached in terms of structural time-series models, in which the different components (states) have a financial interpretation. The parameters of these models can be estimated using maximum likelihood. This approach results in a non-linear parameter estimation problem and thus a key issue is how to obtain reliable initial estimates. In this paper, we focus on the initial parameter estimation problem for the Schwartz-Smith two-factor model commonly used in asset valuation. We propose the use of a two-step method. The first step considers a univariate model based only on the spot price and uses a transfer function model to obtain initial estimates of the fundamental parameters. The second step uses the estimates obtained in the first step to initialize a re-parameterized state-space-innovations based estimator, which includes information related to future prices. The second step refines the estimates obtained in the first step and also gives estimates of the remaining parameters in the model. This paper is part tutorial in nature and gives an introduction to aspects of commodity price modeling and the associated parameter estimation problem.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.