High Dimensional Sparse Econometric Models: An Introduction

In this chapter we discuss conceptually high dimensional sparse econometric models as well as estimation of these models using L1-penalization and post-L1-penalization methods. Focusing on linear and nonparametric regression frameworks, we discuss various econometric examples, present basic theoretical results, and illustrate the concepts and methods with Monte Carlo simulations and an empirical application. In the application, we examine and confirm the empirical validity of the Solow-Swan model for international economic growth

Concentration Inequalities and Confidence Bands for Needlet Density Estimators on Compact Homogeneous Manifolds

Let $X_1,...,X_n$ be a random sample from some unknown probability density $f$ defined on a compact homogeneous manifold $\mathbf M$ of dimension $d \ge 1$. Consider a 'needlet frame' $\{\phi_{j \eta}\}$ describing a localised projection onto the space of eigenfunctions of the Laplace operator on $\mathbf M$ with corresponding eigenvalues less than $2^{2j}$, as constructed in \cite{GP10}. We prove non-asymptotic concentration inequalities for the uniform deviations of the linear needlet density estimator $f_n(j)$ obtained from an empirical estimate of the needlet projection $\sum_\eta \phi_{j \eta} \int f \phi_{j \eta}$ of $f$. We apply these results to construct risk-adaptive estimators and nonasymptotic confidence bands for the unknown density $f$. The confidence bands are adaptive over classes of differentiable and H\"{older}-continuous functions on $\mathbf M$ that attain their H\"{o}lder exponents.Comment: Probability Theory and Related Fields, to appea

Quantile regression with group lasso for classification

Applications of regression models for binary response are very common and models specific to these problems are widely used. Quantile regression for binary response data has recently attracted attention and regularized quantile regression methods have been proposed for high dimensional problems. When the predictors have a natural group structure, such as in the case of categorical predictors converted into dummy variables, then a group lasso penalty is used in regularized methods. In this paper, we present a Bayesian Gibbs sampling procedure to estimate the parameters of a quantile regression model under a group lasso penalty for classification problems with a binary response. Simulated and real data show a good performance of the proposed method in comparison to mean-based approaches and to quantile-based approaches which do not exploit the group structure of the predictors

Modelling and optimization by response surface methodology of chitin extraction From Parapenaeus longirostris by Lactobacillus helveticus

International audienc

Optimization of medium composition for enhanced chitin extraction from Parapenaeus longirostris by Lactobacillus helveticus using response surface methodology

International audienceChitin extraction by biological way, using the lactobacilli Lactobacillus helveticus, is a non-polluting method and offers the opportunity to preserve the exceptional qualities of chitin and its derivatives. However, the major disadvantage of the fermentative way is the low efficiency of demineralization and deproteinization. The aim of our study is to improve the yield of extraction. Many factors, such as the initial concentration of carbon source, fermentation time, incubation temperature, inoculum size, shell size, volume and medium composition have been reported to influence the fermentation process and consequently demineralization and deproteinization efficiency. Based on the use of central composite design and response surface methodology ten factors with three levels each were examined to determine the optimal operational conditions of demineralization and deproteinization. The analysis of the obtained results showed that the optimal conditions of 98% of demineralization and 78% of deproteinisation are 171.4 g L−1 of reducing sugars, 2.03 g of nitrogen source [(NH4)2Fe(SO4)2] and 1.29 g of calcium source (CaCl2), used to ferment 4.84 g of shells, of 1.053 mm size heat treated at 120 °C, with 10 mL of inoculum (L. helveticus) incubated at 32.1 °C in 100 mL of juice date for 254.38 h (15 days)

Economic study of the treatment of surface water by small ultrafiltration units

The purpose of this work is to evaluate the possibility of utilising an ultrafiltration process for the treatment of water from the dam in the Kabylia region of Algeria and, in particular, for the provision of drinking water to people living in dispersed small villages. The water quality was determined by measuring turbidity, and natural organic matter concentration. The results obtained with an ultrafiltration process indicate that this technique can considerably reduce suspended and organic matter. It also improves the bacteriological quality of the treated water. An economic evaluation for ultrafiltration of surface water is presented. The economic study was performed for a drinking water unit of 20 m3/h . It was found that the cost per m3 of treated water ($ 0.235/m3) obtained would not be excessively high for the states of the North African region. WaterSA Vol.27(2) 2001: 199-20

Utilisation of factorial experiments for the UV/H2O2 process in a batch reactor

Factorial experiments provide a comprehensive understanding of the impact of operational variables on process performance. Utilisation of the Hadamard matrix taking into account all interaction effects, appeared to be efficient for giving a mathematical model that conformed to criteria validity. The predictions given by the factorial experiments model were confirmed by the experiments. Phenol oxidative degradation kinetics were not significantly influenced by pH or hardness of the solution to be treated, as is predicted by factorial experiments. On the other hand, initial H2O2 concentration, initial phenol concentration and temperature significantly influenced the efficiency of the process. Optimal values were determined: a temperature of about 20°C and a CH2O2/Cphenol ratio of 120 (mg/mg). WaterSA Vol.27(4) 2001: 551-55

Biocorrosion of carbon steel by sulfate-reducing consortium obtained from an algerian oil field that utilize nitrate

Biooxidation of sulphide under denitrifying conditions is a key process in control of souring in oil reservoirs and in treatment of gas and liquids contaminated with sulphide and nitrate. The effect of nitrate injection on the microbial community has already been evaluated in offshore oil industry production, but has never been studied in onshore such as Algerian oilfield. In this work, the SRB consortiums isolated by inoculating saline Postgate's medium C with injected water obtained from the In Amenas oil field, situated in the South Eastern Algerian Sahara was tested in the presence of sulfate, when nitrate was dosed at 120 mg/l it was reduced by this consortium bacteria, with some ammonium production. Therefore, this mechanism could be important in oilfield systems where nitrate is applied to prevent sulfide generation by SRB which leads to reservoir souring. In static tests the influence of this SRB consortium bacterium on corrosion was assessed using carbon steel coupons, in the presence of sulfate and in the presence of sulfate with 120 mg/l nitrate. Furthermore, the occurrence of pitting corrosion was fairly low under this circumstanc