A Double Diamond Comparison of the Automotive Industry of China, India, and South Korea

Recently China became the third largest automotive producing country in the world, next to the U.S and Japan. South Korea is the fifth biggest automotive manufacturing country and India has more recently emerged as one of the top ten automotive manufacturing countries. This paper compares industry competitiveness of these three emerging automotive manufacturing countries by using the Double Diamond Model which is based on Porter\u27s Diamond Model. Our results show that the Chinese automotive industry is as competitive as South Korea\u27s factor conditions, demand conditions, related and supporting industries as well as competitive rivalry. By contrast, India is less competitive

Robust Lasso-Zero for sparse corruption and model selection with missing covariates

We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology [Descloux and Sardy, 2018], initially introduced for sparse linear models, to the sparse corruptions problem. We give theoretical guarantees on the sign recovery of the parameters for a slightly simplified version of the estimator, called Thresholded Justice Pursuit. The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates. In addition to not requiring the specification of a model for the covariates, nor estimating their covariance matrix or the noise variance, the method has the great advantage of handling missing not-at random values without specifying a parametric model. Numerical experiments and a medical application underline the relevance of Robust Lasso-Zero in such a context with few available competitors. The method is easy to use and implemented in the R library lass0