Finite Sample Simulation Based Inference In Var Models With Applications To Order Selection And Causality Testing
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Finite Sample Simulation Based Inference In Var Models With Applications To Order Selection And Causality Testing
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Author : Dufour, Jean-Marie
language : en
Publisher: Montréal : CIRANO
Release Date : 2005
Finite Sample Simulation Based Inference In Var Models With Applications To Order Selection And Causality Testing written by Dufour, Jean-Marie and has been published by Montréal : CIRANO this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Autoregression (Statistics) categories.
Finite Sample Simulation Based Inference In Var Models With Applications To Order Selection And Causality Testing
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Author : Jean-Marie Dufour
language : en
Publisher: Centre interuniversitaire de recherche en économie quantitative
Release Date : 2005*
Finite Sample Simulation Based Inference In Var Models With Applications To Order Selection And Causality Testing written by Jean-Marie Dufour and has been published by Centre interuniversitaire de recherche en économie quantitative this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005* with Autoregression (Statistics) categories.
Latent Variable Modeling And Applications To Causality
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Author : Maia Berkane
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Latent Variable Modeling And Applications To Causality written by Maia Berkane and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.
Aanwinsten Van De Centrale Bibliotheek Queteletfonds
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Author : Bibliothèque centrale (Fonds Quetelet)
language : en
Publisher:
Release Date : 1998
Aanwinsten Van De Centrale Bibliotheek Queteletfonds written by Bibliothèque centrale (Fonds Quetelet) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Agent Based Models And Causal Inference
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Author : Gianluca Manzo
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-28
Agent Based Models And Causal Inference written by Gianluca Manzo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-28 with Mathematics categories.
Agent-based Models and Causal Inference Scholars of causal inference have given little credence to the possibility that ABMs could be an important tool in warranting causal claims. Manzo’s book makes a convincing case that this is a mistake. The book starts by describing the impressive progress that ABMs have made as a credible methodology in the last several decades. It then goes on to compare the inferential threats to ABMs versus the traditional methods of RCTs, regression, and instrumental variables showing that they have a common vulnerability of being based on untestable assumptions. The book concludes by looking at four examples where an analysis based on ABMs complements and augments the evidence for specific causal claims provided by other methods. Manzo has done a most convincing job of showing that ABMs can be an important resource in any researcher’s tool kit. Christopher Winship, Diker-Tishman Professor of Sociology, Harvard University, USA Agent-based Models and Causal Inference is a first-rate contribution to the debate on, and practice of, causal claims. With exemplary rigor, systematic precision and pedagogic clarity, this book contrasts the assumptions about causality that undergird agent-based models, experimental methods, and statistically based observational methods, discusses the challenges these methods face as far as inferences go, and, in light of this discussion, elaborates the case for combining these methods’ respective strengths: a remarkable achievement. Ivan Ermakoff, Professor of Sociology, University of Wisconsin-Madison, USA Agent-based models are a uniquely powerful tool for understanding how patterns in society may arise in often surprising and counter-intuitive ways. This book offers a strong and deeply reflected argument for how ABM’s can do much more: add to actual empirical explanation. The work is of great value to all social scientists interested in learning how computational modelling can help unraveling the complexity of the real social world. Andreas Flache, Professor of Sociology at the University of Groningen, Netherlands Agent-based Models and Causal Inference is an important and much-needed contribution to sociology and computational social science. The book provides a rigorous new contribution to current understandings of the foundation of causal inference and justification in the social sciences. It provides a powerful and cogent alternative to standard statistical causal-modeling approaches to causation. Especially valuable is Manzo’s careful analysis of the conditions under which an agent-based simulation is relevant to causal inference. The book represents an exceptional contribution to sociology, the philosophy of social science, and the epistemology of simulations and models. Daniel Little, Professor of philosophy, University of Michigan, USA Agent-based Models and Causal Inference delivers an insightful investigation into the conditions under which different quantitative methods can legitimately hold to be able to establish causal claims. The book compares agent-based computational methods with randomized experiments, instrumental variables, and various types of causal graphs. Organized in two parts, Agent-based Models and Causal Inference connects the literature from various fields, including causality, social mechanisms, statistical and experimental methods for causal inference, and agent-based computation models to help show that causality means different things within different methods for causal analysis, and that persuasive causal claims can only be built at the intersection of these various methods. Readers will also benefit from the inclusion of: A thorough comparison between agent-based computation models to randomized experiments, instrumental variables, and several types of causal graphs A compelling argument that observational and experimental methods are not qualitatively superior to simulation-based methods in their ability to establish causal claims Practical discussions of how statistical, experimental and computational methods can be combined to produce reliable causal inferences Perfect for academic social scientists and scholars in the fields of computational social science, philosophy, statistics, experimental design, and ecology, Agent-based Models and Causal Inference will also earn a place in the libraries of PhD students seeking a one-stop reference on the issue of causal inference in agent-based computational models.
Autoregressive Model Inference In Finite Samples
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Author : Hans Einar Wensink
language : en
Publisher:
Release Date : 1996
Autoregressive Model Inference In Finite Samples written by Hans Einar Wensink and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Mathematics categories.
Inference In Hidden Markov Models
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Author : Olivier Cappé
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-12
Inference In Hidden Markov Models written by Olivier Cappé and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-12 with Mathematics categories.
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
Journal Of Econometrics
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Author :
language : en
Publisher:
Release Date : 1997
Journal Of Econometrics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Econometrics categories.
Model Selection And Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Model Selection And Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
Statisticians and applied scientists must often select a model to fit empirical data. This book discusses the philosophy and strategy of selecting such a model using the information theory approach pioneered by Hirotugu Akaike. This approach focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book includes practical applications in biology and environmental science.
Model Selection And Multimodel Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-12-04
Model Selection And Multimodel Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-04 with Mathematics categories.
A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.