What do we mean by inequality comparisons? If the rich just get richer and the poor get poorer, the answer might seem easy. But what if the income distribution changes in a complicated way? Can we ...use mathematical or statistical techniques to simplify the comparison problem in a way that has economic meaning? What does it mean to measure inequality? Is it similar to National Income? Or price index? Is it enough just to work out the Gini coefficient? This book tackles these questions and examines the underlying principles of inequality measurement and its relation to welfare economics, distributional analysis, and information theory. The book covers modern theoretical developments in inequality analysis, as well as showing how the way we think about inequality today has been shaped by classic contributions in economics and related disciplines. Formal results and detailed literature discussion are provided in two appendices. The principal points are illustrated in the main text, using examples from US and UK data, as well as other data sources, and associated web materials provide hands-on learning.
The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a ...user-friendly interface, an interactive 3-D visualization, and a wide selection of algorithms. This practical book provides a step-by-step guide to using SGeMS algorithms. It explains the underlying theory, demonstrates their implementation, discusses their potential limitations, and helps the user make an informed decision about the choice of one algorithm over another. Users can complete complex tasks using the embedded scripting language, and new algorithms can be developed and integrated through the SGeMS plug-in mechanism. SGeMS was the first software to provide algorithms for multiple-point statistics, and the book presents a discussion of the corresponding theory and applications. Incorporating the full SGeMS software (now available from www.cambridge.org/9781107403246), this book is a useful user-guide for Earth Science graduates and researchers, as well as practitioners of environmental mining and petroleum engineering.
The third edition of Van Kampen's standard work has been revised and updated. The main difference with the second edition is that the contrived application of the quantum master equation in section 6 ...of chapter XVII has been replaced with a satisfactory treatment of quantum fluctuations. Apart from that throughout the text corrections have been made and a number of references to later developments have been included. From the recent textbooks the following are the most relevant. C.W.Gardiner, Quantum Optics (Springer, Berlin 1991)D.T. Gillespie, Markov Processes (Academic Press, San Diego 1992)W.T. Coffey, Yu.P.Kalmykov, and J.T.Waldron, The Langevin Equation (2nd edition, World Scientific, 2004)
* Comprehensive coverage of fluctuations and stochastic methods for describing them* A must for students and researchers in applied mathematics, physics and physical chemistry
Here, by popular demand, is the updated edition to Joel Best's classic guide to understanding how numbers can confuse us. In his new afterword, Best uses examples from recent policy debates to ...reflect on the challenges to improving statistical literacy. Since its publication ten years ago, Damned Lies and Statistics has emerged as the go-to handbook for spotting bad statistics and learning to think critically about these influential numbers.
Spatial Analysis Fortin, Marie-Josée; Dale, Mark R. T.
04/2005
eBook
The spatial and temporal dimensions of ecological phenomena have always been inherent in the conceptual framework of ecology, but only recently have they been incorporated explicitly into ecological ...theory, sampling design, experimental design and models. Statistical techniques for spatial analysis of ecological data are burgeoning and many ecologists are unfamiliar with what is available and how the techniques should be used correctly. This book gives an overview of the wide range of spatial statistics available to analyse ecological data, and provides advice and guidance for graduate students and practising researchers who are either about to embark on spatial analysis in ecological studies or who have started but are unsure how to proceed. Only a basic understanding of statistics is assumed and many schematic illustrations are given to complement or replace mathematical technicalities, making the book accessible to ecologists wishing to enter this important and fast-growing field for the first time.
A crucial challenge for economists is figuring out how people interpret the world and form expectations that will likely influence their economic activity. Inflation, asset prices, exchange rates, ...investment, and consumption are just some of the economic variables that are largely explained by expectations. Here George Evans and Seppo Honkapohja bring new explanatory power to a variety of expectation formation models by focusing on the learning factor. Whereas the rational expectations paradigm offers the prevailing method to determining expectations, it assumes very theoretical knowledge on the part of economic actors. Evans and Honkapohja contribute to a growing body of research positing that households and firms learn by making forecasts using observed data, updating their forecast rules over time in response to errors. This book is the first systematic development of the new statistical learning approach.
Depending on the particular economic structure, the economy may converge to a standard rational-expectations or a "rational bubble" solution, or exhibit persistent learning dynamics. The learning approach also provides tools to assess the importance of new models with expectational indeterminacy, in which expectations are an independent cause of macroeconomic fluctuations. Moreover, learning dynamics provide a theory for the evolution of expectations and selection between alternative equilibria, with implications for business cycles, asset price volatility, and policy. This book provides an authoritative treatment of this emerging field, developing the analytical techniques in detail and using them to synthesize and extend existing research.
Using data samples collected with the BESIII detector at center-of-mass energies √s=4.23, 4.26, 4.36, and 4.42 GeV, we measure the branching fractions of ηc → K+K−π0, KS0K±π∓, 2(π+π−π0), and pp¯, via ...the process e+e− → π+π−hc, hc → γηc. The corresponding results are (1.15±0.12±0.10)%, (2.60±0.21±0.20)%, (15.2±1.8±1.7)%, and (0.120±0.026±0.015)%, respectively. Here the first uncertainties are statistical, and the second ones systematic. Additionally, the charged track multiplicity of ηc decays is measured for the first time.
Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of ...knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.
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