Variable Conditions

First Published in 2013. Routledge is an imprint of Taylor & Francis, an informa company.

There are still insufficient general theories on the law of diminishing returns, despite 100 years of development. Starting with intensive variables theory, and by utilizing tools of spatiotemporal correlation and intensive functions, moving on to the integrated curve of diminishing returns and intensive theory, and even more importantly, using a combination of static and dynamic GIS, and integrating numerical calculation and spatial optimization, this book not only creates a unique theoretical framework and methodology for the evaluation of land use effect, but also addresses the long-standing lack of universal theories and methods on the law of diminishing returns. It will have far-reaching impacts on the development of this area and its practical application. The book covers a wide range fields in geography, land science, geographic information science, management science and related areas. Novel theoretical perspectives illustrated with many detailed case studies offer an easier way for readers to expand their research, ensuring that both academic and business audiences will benefit. Prof. Xinqi Zheng works at the China University of Geosciences (Beijing), People’s Republic of China.

"This book is organized around three concepts fundamental to OS construction: virtualization (of CPU and memory), concurrency (locks and condition variables), and persistence (disks, RAIDS, and file systems"--Back cover.

Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of both applications, describing scenarios in which accuracy and speed are the primary goals. Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.

This important collection brings together leading econometricians to discuss advances in the areas of the econometrics of panel data. The papers in this collection can be grouped into two categories. The first, which includes chapters by Amemiya, Baltagi, Arellano, Bover and Labeaga, primarily deal with different aspects of limited dependent variables and sample selectivity. The second group of papers, including those by Nerlove, Schmidt and Ahn, Kiviet, Davies and Lahiri, consider issues that arise in the estimation of dyanamic (possibly) heterogeneous panel data models. Overall, the contributors focus on the issues of simplifying complex real-world phenomena into easily generalisable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited dependent variables and panel data were particularly influential, it is a fitting tribute that this volume is dedicated to him.