000			01498 a2200169 4500
999			_c26102 _d26102
020			_a9781316631140
082			_a001.42 _bARO-F
100			_aAronow M. Peter
100			_aMiller T. Benjamin
245			_aFoundations of agnostic statistics
260			_bCambridge University Press _c2019 _aNew York
300			_axv, 298p.
504			_aInclude Reference and Index
520			_aReflecting 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
650			_aQuantitative research _vStatistics
942			_2ddc _cBK