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"Economics -- Miscellanea"
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The first book to explore the hot new science of networks and their impact on nature, business, medicine, and everyday life.
eBook
Prescribing by numbers : drugs and the definition of disease
Winner, 2009 Rachel Carson Prize, Society for the Social Studies of ScienceWinner, 2012 Edward Kremers Award, American Institute of the History of Pharmacy
The second half of the twentieth century witnessed the emergence of a new model of chronic disease—diagnosed on the basis of numerical deviations rather than symptoms and treated on a preventive basis before any overt signs of illness develop—that arose in concert with a set of safe, effective, and highly marketable prescription drugs. In Prescribing by Numbers, physician-historian Jeremy A. Greene examines the mechanisms by which drugs and chronic disease categories define one another within medical research, clinical practice, and pharmaceutical marketing, and he explores how this interaction has profoundly altered the experience, politics, ethics, and economy of health in late-twentieth-century America.
Prescribing by Numbers highlights the complex historical role of pharmaceuticals in the transformation of disease categories. Greene narrates the expanding definition of the three principal cardiovascular risk factors—hypertension, diabetes, and high cholesterol—each intersecting with the career of a particular pharmaceutical agent. Drawing on documents from corporate archives and contemporary pharmaceutical marketing literature in concert with the clinical literature and the records of researchers, clinicians, and public health advocates, Greene produces a fascinating account of the expansion of the pharmaceutical treatment of chronic disease over the past fifty years.
While acknowledging the influence of pharmaceutical marketing on physicians, Greene avoids demonizing drug companies. Rather, his provocative and comprehensive analysis sheds light on the increasing presence of the subjectively healthy but highly medicated individual in the American medical landscape, suggesting how historical analysis can help to address the problems inherent in the program of pharmaceutical prevention.
eBook
Home hacks : cleaning, storage & organizing, decorating, gardening, entertaining, clothing care, food & cooking, health & safety, appliances & gadgets, easy repairs
Discover the hundreds of practical Do-It-Yourself home improvement and household hacks to simplify everyday life. --Publisher
Book
A NEW STUDY ON ASYMPTOTIC OPTIMALITY OF LEAST SQUARES MODEL AVERAGING
In this article, we present a comprehensive study of asymptotic optimality of least squares model averaging methods. The concept of asymptotic optimality is that in a large-sample sense, the method results in the model averaging estimator with the smallest possible prediction loss among all such estimators. In the literature, asymptotic optimality is usually proved under specific weights restriction or using hardly interpretable assumptions. This article provides a new approach to proving asymptotic optimality, in which a general weight set is adopted, and some easily interpretable assumptions are imposed. In particular, we do not impose any assumptions on the maximum selection risk and allow a larger number of regressors than that of existing studies.
Journal Article
ON THE CONVERGENCE RATE OF POTENTIALS OF BRENIER MAPS
The theory of optimal transportation has experienced a sharp increase in interest in many areas of economic research such as optimal matching theory and econometric identification. A particularly valuable tool, due to its convenient representation as the gradient of a convex function, has been the Brenier map: the matching obtained as the optimizer of the Monge–Kantorovich optimal transportation problem with the euclidean distance as the cost function. Despite its popularity, the statistical properties of the Brenier map have yet to be fully established, which impedes its practical use for estimation and inference. This article takes a first step in this direction by deriving a convergence rate for the simple plug-in estimator of the potential of the Brenier map via the semi-dual Monge–Kantorovich problem. Relying on classical results for the convergence of smoothed empirical processes, it is shown that this plug-in estimator converges in standard deviation to its population counterpart under the minimax rate of convergence of kernel density estimators if one of the probability measures satisfies the Poincaré inequality. Under a normalization of the potential, the result extends to convergence in the
$L^2$
norm, while the Poincaré inequality is automatically satisfied. The main mathematical contribution of this article is an analysis of the second variation of the semi-dual Monge–Kantorovich problem, which is of independent interest.
Journal Article