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Benjamin T. Miller

Kirjat ja teokset yhdessä paikassa: 2 kirjaa, julkaisuja vuodelta 2019, suosituimpien joukossa Foundations of Agnostic Statistics. Vertaile teosten hintoja ja tarkista saatavuus suomalaisista kirjakaupoista.

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Foundations of Agnostic Statistics

Foundations of Agnostic Statistics

Peter M. Aronow; Benjamin T. Miller

Cambridge University Press
2019
pokkari
Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.
Foundations of Agnostic Statistics

Foundations of Agnostic Statistics

Peter M. Aronow; Benjamin T. Miller

Cambridge University Press
2019
sidottu
Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.