Causal Inference in Pharmaceutical Statistics, Fang, Yixin

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Imbens
Название: Causal Inference for Statistics, Social, and Biomedical Sciences
ISBN: 0521885884 ISBN-13(EAN): 9780521885881
Издательство: Cambridge Academ
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Описание: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Автор: Morgan
Название: Counterfactuals and Causal Inference
ISBN: 1107694167 ISBN-13(EAN): 9781107694163
Издательство: Cambridge Academ
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Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.

Автор: B. Sreenivasulu et al.
Название: Causality Tests In Econometrics: Choice of Causal Variables
ISBN: 3659504041 ISBN-13(EAN): 9783659504044
Издательство: LAP LAMBERT Academic Publishing
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Описание: In the Present Book Chapter-I is an introductory one.Chapter-II describes the concept and causal relations by econometric models. It presents the different representations such as autoregressive, Moving – average and univariate representation of causality. Chapter-III explore lucidly the various tests for causality, we come across in econometrics. In regression analysis, researchers are interested in testing for the exogenity of variables this testing is closely related to the causality test proposed by Granger, which is explained in detail in this chapter. Chapter-IV gives the conclusions about the present study.The various relevant research articles have been presented under the title BIBLIOGRAPHY.

Автор: Hooshang Nayebi
Название: Advanced Statistics for Testing Assumed Causal Relationships
ISBN: 3030547531 ISBN-13(EAN): 9783030547530
Издательство: Springer
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Описание: It presents that potential effects of each independent variable on the dependent variable are not limited to direct and indirect effects. The path analysis shows each independent variable has a pure effect on the dependent variable. So, it can be shown the unique contribution of each independent variable to the variation of the dependent variable.

Автор: Mark J. van der Laan; Sherri Rose
Название: Targeted Learning in Data Science
ISBN: 3319653032 ISBN-13(EAN): 9783319653037
Издательство: Springer
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Описание: This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data.

Автор: Bibhas Chakraborty; Erica E.M. Moodie
Название: Statistical Methods for Dynamic Treatment Regimes
ISBN: 1489990305 ISBN-13(EAN): 9781489990303
Издательство: Springer
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Цена: 6986.00 р.
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Описание: Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine.

Автор: Burgess, Stephen (mrc Biostatistics Unit, Cambridge, Uk) Thompson, Simon G. (department Of Public Health And Primary Care, University Of Cambridge, Uk
Название: Mendelian randomization
ISBN: 1032019514 ISBN-13(EAN): 9781032019512
Издательство: Taylor&Francis
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Описание: Mendelian randomization (MR) uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.

Автор: Daniels, Michael J.
Название: Bayesian nonparametrics for causal inference and missing data
ISBN: 036734100X ISBN-13(EAN): 9780367341008
Издательство: Taylor&Francis
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Автор: Aki Vehtari, Andrew Gelman
Название: Active Statistics: Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference
ISBN: 100943621X ISBN-13(EAN): 9781009436212
Издательство: Cambridge Academ
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Описание: This book provides statistics instructors and students with complete classroom material for a one- or two-semester course on applied regression and causal inference. It is built around 52 stories, 52 class-participation activities, 52 hands-on computer demonstrations, and 52 discussion problems that allow instructors and students to explore in a fun way the real-world complexity of the subject. The book fosters an engaging 'flipped classroom' environment with a focus on visualization and understanding. The book provides instructors with frameworks for self-study or for structuring the course, along with tips for maintaining student engagement at all levels, and practice exam questions to help guide learning. Designed to accompany the authors' previous textbook Regression and Other Stories, its modular nature and wealth of material allow this book to be adapted to different courses and texts or be used by learners as a hands-on workbook.

Автор: Ding, Peng
Название: A First Course in Causal Inference
ISBN: 1032758627 ISBN-13(EAN): 9781032758626
Издательство: Taylor&Francis
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Название: Statistical Methods for Dynamic Treatment Regimes
ISBN: 1461474272 ISBN-13(EAN): 9781461474272
Издательство: Springer
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Цена: 11179.00 р.
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Описание: Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine.