Quantitative risk analysis using vulnerability indicators to assess food insecurity in the Niayes agricultural region of West Senegal

There is an increasing need to develop indicators of vulnerability and adaptive capacity to determine the robustness of response strategies over time and better understand the underlying processes. This study aimed to determine levels of risk of food insecurity using defined vulnerability indicators. For the purpose of this study, factors influencing food insecurity and different vulnerable indicators were examined using quantitative and qualitative research methods. Observations made on the physical environment (using tools for spatial analysis) and socio-economic surveys conducted with local populations have quantified vulnerability indicators in the Niayes agricultural region. Application of the Classification and Regression Tree (CART) model has enabled us to quantify the level of vulnerability of the zone. The results show that the decrease in agricultural surface areas is the most discriminant one in this study. The speed of reduction of the agricultural areas has specially increased between 2009 and 2014, with a loss of 65% of these areas. Therefore, a decision-making system, centred on the need for reinforcing the resilience of local populations, by preserving the agricultural vocation of the Niayes region and even in the Sahelian regions requires support and extension services for the farmers in order to promote sustainable agricultural practices.https://doi.org/10.4102/jamba.v9i1.37

A Consistent Nonparametric Test of the Convexity of Regression Based on Least Squares Splines

This paper provides a test of convexity of a regression function. This test is based on the least squares splines. The test statistic is shown to be asymptotically of size equal to the nominal level, while diverging to infinity if the convexity is misspecified. Therefore, the test is consistent against all deviations from the null hypothesis

A nonparametric test of the non-convexity of regression

This paper proposes a nonparametric test of the non-convexity of a smooth regression function based on least squares or hybrid splines. By a simple formulation of the convexity hypothesis in the class of all polynomial cubic splines, we build a test which has an asymptotic size equal to the nominal level. It is shown that the test is consistent and is robust to nonnormality. The behavior of the test under the local alternatives is studied

A Consistent Nonparametric Test of the Convexity of Regression Based on Least Squares Splines

This paper provides a test of convexity of a regression function. This test is based on the least squares splines. The test statistic is shown to be asymptotically of size equal to the nominal level, while diverging to inønity if the convexity is misspeciøed. Therefore, the test is consistent against all deviations from the null hypothesis. 1 INTRODUCTION Tests of convexity of a regression function is one of the most important problems in econometrics. Indeed, iThe General Theory of Employment, Interest, and Money emphasized the central importance of the consumption function and explicitly argued that the consumption function is concavej (Carroll & Kimball 1996). Economic theory predicts also the convexity of functions like for example Bernoulli utility function, cost function, production function, Engels curves, ... Otherwise, the Human Capital theory argued that the relationships between the logarithm of wage and the experience is concave. On the other hand, psychologists have worrie..

Pharmacokinetic-pharmacodynamic models for categorical toxicity data

International audienceWe propose a pharmacokinetic-pharmacodynamic (PK/PD) model (with possibly different choices for the PD link) for categorical toxicity data analysis. This is extension of the one-comportment model that applies to toxic endpoints categorised by grades (e.g., benign, mild, severe, and very severe). The model assumes that the area under the curve (AUC) of the internal quantity of the chemical substance is the critical dose-metric that drives the acute toxic phenomenon. That model handles time-varying concentrations and takes into account follow-up time, i.e., time at which effects are observed. Moreover the model bridges mechanistically based dose-response models and standard dose-response models, retaining the advantages of both. We use Markov chain-Monte Carlo (MCMC) simulations to fit the model to mortality data for mice exposed to chlorine, rats exposed to ammonia, and categorical data (different severity levels) from acute exposures of rats and humans to hydrogen sulfide