Léopold Simar

Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. Each chapter features hands-on exercises that showcase applications across various fields of multivariate data analysis. These exercises utilize high-dimensional to ultra-high-dimensional data, reflecting real-world challenges in big data analysis. For this new edition, the book has been updated and revised and now includes new chapters on modern machine learning techniques for dimension reduction and data visualization, namely locally linear embedding, t-distributed stochastic neighborhood embedding, and uniform manifold approximation and projection, which overcome the shortcomings of traditional visualization and dimension reduction techniques. Solutions to the book’s exercises are supplemented by R and MATLAB or SAS computer code and are available online on the Quantlet and Quantinar platforms. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W. K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.

Estimation and Inference in Nonparametric Frontier Models provides a thorough examination of this topic for students and researchers alike. While nonparametric estimators are widely used to estimate the Productive efficiency of firms and other organizations, it is often done without any attempt to make statistical inference. Recent work has provided statistical properties of these estimators and methods for making statistical inference has established a link between frontier estimation and extreme value theory. New estimators that avoid many of the problems inherent with traditional efficiency estimators have been developed and these new estimators are robust with respect to outliers and avoid the problem of dimensionality. In addition, statistical properties, including asymptotic distributions, of the new estimators are uncovered. Finally, the authors show several approaches for introducing environmental variables into Production models.

Providing a systematic and comprehensive treatment of recent developments in efficiency analysis, this readable book makes available an intuitive yet rigorous presentation of advanced nonparametric and robust methods. This flexible toolbox can be used in theories based on the neoclassical theory of production and its alternatives, including evolutionary theories. The methods are complemented by empirical analysis of three different economic fields: scientific research, mutual funds industry and the insurance sector. The research demonstrates the utility of the toolbox for a wide range of economic issues, including the analysis of economies of scale and scope, dynamics of age and agglomeration effects, trade-offs in production and service activities, and explanations of efficiency differentials. Of interest to applied economists broadly, this book will also be of interest to those focused on Operations Research and/or Management Science.

Providing a systematic and comprehensive treatment of recent developments in efficiency analysis, this readable book makes available an intuitive yet rigorous presentation of advanced nonparametric and robust methods. This flexible toolbox can be used in theories based on the neoclassical theory of production and its alternatives, including evolutionary theories. The methods are complemented by empirical analysis of three different economic fields: scientific research, mutual funds industry and the insurance sector. The research demonstrates the utility of the toolbox for a wide range of economic issues, including the analysis of economies of scale and scope, dynamics of age and agglomeration effects, trade-offs in production and service activities, and explanations of efficiency differentials. Of interest to applied economists broadly, this book will also be of interest to those focused on Operations Research and/or Management Science.