Consistent Testing for Stochastic Dominance
Author : Yoon-Jae Whang
Publisher :
Page : 0 pages
File Size : 17,19 MB
Release : 2004
Category :
ISBN :
Author : Yoon-Jae Whang
Publisher :
Page : 0 pages
File Size : 17,19 MB
Release : 2004
Category :
ISBN :
Author : P. C. B. Phillips
Publisher : Cambridge University Press
Page : 390 pages
File Size : 45,3 MB
Release : 2006-01-09
Category : Business & Economics
ISBN : 9780521807234
The essays in this book explore important theoretical and applied advances in econometrics.
Author : Oliver B. Linton
Publisher :
Page : 50 pages
File Size : 11,56 MB
Release : 2008
Category :
ISBN :
We study a very general setting, and propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of First and Second Order Stochastic Dominance due to McFadden (1989) in the general k-prospect case. We allow for the observations to be generally serially dependent and, for the first time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that the resulting data tests are consistent.
Author : Richard R. Nelson
Publisher : Harvard University Press
Page : 456 pages
File Size : 32,38 MB
Release : 1985-10-15
Category : Business & Economics
ISBN : 9780674041431
This book contains the most sustained and serious attack on mainstream, neoclassical economics in more than forty years. Nelson and Winter focus their critique on the basic question of how firms and industries change overtime. They marshal significant objections to the fundamental neoclassical assumptions of profit maximization and market equilibrium, which they find ineffective in the analysis of technological innovation and the dynamics of competition among firms. To replace these assumptions, they borrow from biology the concept of natural selection to construct a precise and detailed evolutionary theory of business behavior. They grant that films are motivated by profit and engage in search for ways of improving profits, but they do not consider them to be profit maximizing. Likewise, they emphasize the tendency for the more profitable firms to drive the less profitable ones out of business, but they do not focus their analysis on hypothetical states of industry equilibrium. The results of their new paradigm and analytical framework are impressive. Not only have they been able to develop more coherent and powerful models of competitive firm dynamics under conditions of growth and technological change, but their approach is compatible with findings in psychology and other social sciences. Finally, their work has important implications for welfare economics and for government policy toward industry.
Author : William D. Penny
Publisher : Elsevier
Page : 689 pages
File Size : 47,54 MB
Release : 2011-04-28
Category : Psychology
ISBN : 0080466508
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. - An essential reference and companion for users of the SPM software - Provides a complete description of the concepts and procedures entailed by the analysis of brain images - Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data - Stands as a compendium of all the advances in neuroimaging data analysis over the past decade - Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes - Structured treatment of data analysis issues that links different modalities and models - Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 10,5 MB
Release : 2022-06-01
Category : Computers
ISBN : 3031015886
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author : Ilya Molchanov
Publisher : Springer Science & Business Media
Page : 508 pages
File Size : 31,81 MB
Release : 2005-05-11
Category : Mathematics
ISBN : 9781852338923
This is the first systematic exposition of random sets theory since Matheron (1975), with full proofs, exhaustive bibliographies and literature notes Interdisciplinary connections and applications of random sets are emphasized throughout the book An extensive bibliography in the book is available on the Web at http://liinwww.ira.uka.de/bibliography/math/random.closed.sets.html, and is accompanied by a search engine
Author : Olivier Thas
Publisher : Springer Science & Business Media
Page : 358 pages
File Size : 46,67 MB
Release : 2010-03-14
Category : Mathematics
ISBN : 0387927107
Provides a self-contained comprehensive treatment of both one-sample and K-sample goodness-of-fit methods by linking them to a common theory backbone Contains many data examples, including R-code and a specific R-package for comparing distributions Emphesises informative statistical analysis rather than plain statistical hypothesis testing
Author : Christopher J. Flinn
Publisher : MIT Press
Page : 321 pages
File Size : 21,53 MB
Release : 2011-02-04
Category : Business & Economics
ISBN : 0262288761
The introduction of a search and bargaining model to assess the welfare effects of minimum wage changes and to determine an “optimal” minimum wage. In The Minimum Wage and Labor Market Outcomes, Christopher Flinn argues that in assessing the effects of the minimum wage (in the United States and elsewhere), a behavioral framework is invaluable for guiding empirical work and the interpretation of results. Flinn develops a job search and wage bargaining model that is capable of generating labor market outcomes consistent with observed wage and unemployment duration distributions, and also can account for observed changes in employment rates and wages after a minimum wage change. Flinn uses previous studies from the minimum wage literature to demonstrate how his model can be used to rationalize and synthesize the diverse results found in widely varying institutional contexts. He also shows how observed wage distributions from before and after a minimum wage change can be used to determine if the change was welfare-improving. More ambitiously, and perhaps controversially, Flinn proposes the construction and formal estimation of the model using commonly available data; model estimates then enable the researcher to determine directly the welfare effects of observed minimum wage changes. This model can be used to conduct counterfactual policy experiments—even to determine “optimal” minimum wages under a variety of welfare metrics. The development of the model and the econometric theory underlying its estimation are carefully presented so as to enable readers unfamiliar with the econometrics of point process models and dynamic optimization in continuous time to follow the arguments. Although most of the book focuses on the case where only the unemployed search for jobs in a homogeneous labor market environment, later chapters introduce on-the-job search into the model, and explore its implications for minimum wage policy. The book also contains a chapter describing how individual heterogeneity can be introduced into the search, matching, and bargaining framework.
Author : Mike Tsionas
Publisher : Academic Press
Page : 434 pages
File Size : 49,94 MB
Release : 2019-06-19
Category : Business & Economics
ISBN : 0128144319
Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts