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Author : Peter M. Aronow,Benjamin T. Miller
Publisher : Cambridge University Press
Page : 317 pages
File Size : 49,6 Mb
Release : 2019-01-31
Category : Mathematics
ISBN : 9781107178915
Provides an introduction to modern statistical theory for social and health scientists while invoking minimal modeling assumptions.
Author : Joseph B. Kadane,Mark J. Schervish,Teddy Seidenfeld
Publisher : Cambridge University Press
Page : 388 pages
File Size : 53,8 Mb
Release : 1999-08-13
Category : Science
ISBN : 0521640113
This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists.
Author : Avrim Blum,John Hopcroft,Ravindran Kannan
Publisher : Cambridge University Press
Page : 433 pages
File Size : 45,9 Mb
Release : 2020-01-23
Category : Computers
ISBN : 9781108485067
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Author : Leonard J. Savage
Publisher : Courier Corporation
Page : 341 pages
File Size : 45,6 Mb
Release : 1972-06-01
Category : Mathematics
ISBN : 9780486623498
Classic analysis of the subject and the development of personal probability; one of the greatest controversies in modern statistcal thought. New preface and new footnotes to 1954 edition, with a supplementary 180-item annotated bibliography by author. Calculus, probability, statistics, and Boolean algebra are recommended.
Author : Leonard Jimmie Savage
Publisher : Unknown
Page : 128 pages
File Size : 49,6 Mb
Release : 1974
Category : Mathematical statistics
ISBN : OCLC:502402006
Author : D.G. Rees
Publisher : CRC Press
Page : 564 pages
File Size : 43,7 Mb
Release : 1987-09-01
Category : Mathematics
ISBN : 0412285606
This text provides a through, straightforward first course on basics statistics. Emphasizing the application of theory, it contains 200 fully worked examples and supplies exercises in each chapter-complete with hints and answers.
Author : Justin Grimmer,Margaret E. Roberts,Brandon M. Stewart
Publisher : Princeton University Press
Page : 360 pages
File Size : 42,6 Mb
Release : 2022-01-04
Category : Social Science
ISBN : 9780691207995
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry
Author : Vikram Dayal,Anand Murugesan
Publisher : Springer Nature
Page : 304 pages
File Size : 54,7 Mb
Release : 2023-09-29
Category : Business & Economics
ISBN : 9789819939053
This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snow’s heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.
Author : Macartan Humphreys,Alan M. Jacobs
Publisher : Cambridge University Press
Page : 435 pages
File Size : 41,8 Mb
Release : 2023-10-31
Category : Social Science
ISBN : 9781107169623
Develops a new approach to the use of causal models for qualitative and mixed-method research design and causal inference.
Author : Graeme Blair,Alexander Coppock,Macartan Humphreys
Publisher : Princeton University Press
Page : 393 pages
File Size : 40,8 Mb
Release : 2023-06-27
Category : Social Science
ISBN : 9780691199580
A state-of-the-art approach to evaluating research design for students and scholars across the social sciences Assessing the properties of research designs before implementing them can be tricky for even the most seasoned researchers. This book provides a powerful framework—Model, Inquiry, Data Strategy, and Answer Strategy, or MIDA—for describing any empirical research design in the social sciences. MIDA enables you to characterize the key analytic features of observational and experimental designs, qualitative and quantitative designs, and descriptive and causal designs. An accompanying algorithm lets you declare designs in the MIDA framework, diagnose properties such as bias and precision, and redesign features like sampling, assignment, measurement, and estimation procedures. Research Design in the Social Sciences is an essential tool kit for the entire life of a research project, from planning and realization of design to the integration of your results into the scientific literature. A must-have resource for current and future researchers who want to learn about the properties of their designs before they implement them Includes a library of the most common designs in the social sciences Provides a complete declaration of the canonical design for each library entry, describes the circumstances under which the design can be strong or weak, and explores the consequences of the choices under the research designer’s control Accompanied by online resources that can be used in conjunction with the book An ideal textbook for graduate students and advanced undergraduates
Author : Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert
Publisher : CRC Press
Page : 500 pages
File Size : 44,8 Mb
Release : 2021-09-15
Category : Mathematics
ISBN : 9781000435634
Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Author : Mario V. Wüthrich,Michael Merz
Publisher : Springer Nature
Page : 611 pages
File Size : 40,7 Mb
Release : 2022-11-22
Category : Mathematics
ISBN : 9783031124099
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
Author : Anonim
Publisher : Unknown
Page : 112 pages
File Size : 48,6 Mb
Release : 1970
Category : Electronic
ISBN : OCLC:919879506
Author : David Diez,Christopher Barr,Mine Çetinkaya-Rundel
Publisher : Unknown
Page : 128 pages
File Size : 44,8 Mb
Release : 2015-07-02
Category : Electronic
ISBN : 1943450048
The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.
Author : Ann J. Hughes,Dennis E. Grawoig
Publisher : Addison Wesley Publishing Company
Page : 552 pages
File Size : 50,5 Mb
Release : 1971
Category : Social sciences
ISBN : UOM:39076005650606