Ecological Inference
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Ecological Inference
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Author : Gary King
language : en
Publisher: Cambridge University Press
Release Date : 2004-09-13
Ecological Inference written by Gary King and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-13 with Nature categories.
Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
A Solution To The Ecological Inference Problem
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Author : Gary King
language : en
Publisher: Princeton University Press
Release Date : 2013-09-20
A Solution To The Ecological Inference Problem written by Gary King and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-20 with Political Science categories.
This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem. King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice. King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem.
Ecological Inference
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Author : Laura Irwin Langbein
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1978-01-01
Ecological Inference written by Laura Irwin Langbein and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978-01-01 with Social Science categories.
This paper considers the possible effects of making inferences about individuals from aggregate data. It assumes a knowledge of regression analysis, and explores the utility of techniques designed to make the inferences in causal modelling more reliable, including a comparison between ecological regression models and ecological correlation.
Solution To The Ecological Inference Problem
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Author :
language : en
Publisher:
Release Date : 1997
Solution To The Ecological Inference Problem 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 categories.
Cf.: http://dx.doi.org/10.3886/ICPSR01132.v1.
Bayesian Inference
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Author : William A Link
language : en
Publisher: Academic Press
Release Date : 2009-08-07
Bayesian Inference written by William A Link and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-07 with Science categories.
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analytical software and examples Leading authors with world-class reputations in ecology and biostatistics
Hierarchical Modeling And Inference In Ecology
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Author : J. Andrew Royle
language : en
Publisher: Elsevier
Release Date : 2008-10-15
Hierarchical Modeling And Inference In Ecology written by J. Andrew Royle and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-15 with Science categories.
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS Computing support in technical appendices in an online companion web site
Mapping Species Distributions
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Author : Janet Franklin
language : en
Publisher: Cambridge University Press
Release Date : 2010-01-07
Mapping Species Distributions written by Janet Franklin and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-07 with Nature categories.
Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.
Hierarchical Modeling And Inference In Ecology
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Author : J. Andrew Royle
language : en
Publisher: Academic Press
Release Date : 2008
Hierarchical Modeling And Inference In Ecology written by J. Andrew Royle and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Science categories.
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site
Cross Level Inference
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Author : Christopher H. Achen
language : en
Publisher: University of Chicago Press
Release Date : 1995-05-15
Cross Level Inference written by Christopher H. Achen and has been published by University of Chicago Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-05-15 with Family & Relationships categories.
In the last several years, new disputes have erupted over the use of group averages from census areas or voting districts to draw inferences about individual social behavior. Social scientists, policy analysts, and historians often have little choice about using this kind of data, but statistical analysis of them is fraught with pitfalls. The recent debates have led to a new menu of choices for the applied researcher. This volume explains why older methods like ecological regression so often fail, and it gives the most comprehensive treatment available of the promising new techniques for cross-level inference. Experts in statistical analysis of aggregate data, Christopher H. Achen and W. Philips Shively contend that cross-level inference makes unusually strong demands on substantive knowledge, so that no one method, such as Goodman's ecological regression, will fit all situations. Criticizing Goodman's model and some recent attempts to replace it, the authors argue for a range of alternate techniques, including estensions of cross-tabular, regression analysis, and unobservable variable estimators.
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan
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Author : Franzi Korner-Nievergelt
language : en
Publisher: Academic Press
Release Date : 2015-04-04
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan written by Franzi Korner-Nievergelt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-04 with Science categories.
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest Written in a step-by-step approach that allows for eased understanding by non-statisticians Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data All example data as well as additional functions are provided in the R-package blmeco