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Random acts of medicine : the hidden forces that sway doctors, impact patients, and shape our health
\"Why do kids born in the summer get diagnosed more often with A.D.H.D.? How are marathons harmful for your health, even when you're not running? What do surgeons and salesmen have in common? Which annual event made people 30 percent more likely to contract COVID-19? As a University of Chicago-trained economist and Harvard medical school professor and doctor, Anupam Jena is uniquely equipped to answer these questions. And as a critical care doctor at Massachusetts General who researches health care policy, Christopher Worsham confronts its impact on the hospital's sickest patients. In this singular work of science and medicine, Jena and Worsham work together to reveal the hidden side of medicine, and its effect on everyone that touches the health care system. Relying on ingeniously devised natural experiments-random events that unknowingly turn us into experimental subjects-Jena and Worsham do more than offer readers colorful stories. They help us see the way our health is shaped by forces invisible to the untrained eye. Do you choose the veteran doctor or the rookie? Do you take the appointment on Monday or on Friday? Do you get the procedure now or wait a week? These questions are rife with significance; their impact can be life changing. In a style that's animated and enlightening, this book empowers you to see past the white coat and find out what really makes medicine work-and how it could work better\"-- Provided by publisher.
Book
An empirical study of supply chain sustainability with financial performances of Indian firms
In this research, we examine empirically the impact of sustainable supply chain practices on financial performances, considering the case of Indian firms. Here, we use a sample of 25 Indian firms listed for their sustainability performances in the Thomson Reuters Environmental, Social and Governance (ESG) scores. The sustainability performance data have been accessed from the Bloomberg terminal, where the overall sustainability performance on ESG is measured as a discounted score on ESG considering various controversies on ESG reported for the firm. And for the study, we associate financial data using the profit indicators of firms. We perceive that the sustainable supply chain practices considering environmental, social and governance performances may not positively impact the financial performance measured by the Return on Asset (ROA) and Return on Equity (ROE), during the considered period of five years for the study. We construct the empirical model and use Partial Least Square (PLS) regression modeling to analyze the results. The study can be further extended for many Indian firms and for firms across different developing economies, as well. The major implications of this research are to observe for firms and their supply chains whether the implementation of Environmental, Social and Governance (ESG) practices can help them in achieving financial benefits, along with other competitive advantages. The research is built on the concept and theory of ecological modernization, which suggests for the economic benefits of environmentalism.
Journal Article
One-dimensional empirical measures, order statistics, and Kantorovich transport distances
This work is devoted to the study of rates of convergence of the empirical measures \\mu_{n} = \\frac {1}{n} \\sum_{k=1}^n \\delta_{X_k}, n \\geq 1, over a sample (X_{k})_{k \\geq 1} of independent identically distributed real-valued random variables towards the common distribution \\mu in Kantorovich transport distances W_p. The focus is on finite range bounds on the expected Kantorovich distances \\mathbb{E}(W_{p}(\\mu_{n},\\mu )) or \\big [ \\mathbb{E}(W_{p}^p(\\mu_{n},\\mu )) \\big ]^1/p in terms of moments and analytic conditions on the measure \\mu and its distribution function. The study describes a variety of rates, from the standard one \\frac {1}{\\sqrt n} to slower rates, and both lower and upper-bounds on \\mathbb{E}(W_{p}(\\mu_{n},\\mu )) for fixed n in various instances. Order statistics, reduction to uniform samples and analysis of beta distributions, inverse distribution functions, log-concavity are main tools in the investigation. Two detailed appendices collect classical and some new facts on inverse distribution functions and beta distributions and their densities necessary to the investigation.
eBook
The environmental effects of the “twin” green and digital transition in European regions
This study explores the nexus between digital and green transformations—the so-called “twin” transition—in European regions in an effort to identify the impact of digital and environmental technologies on the greenhouse gas (GHG) emissions originating from industrial production. We conduct an empirical analysis based on an original dataset that combines information on environmental and digital patent applications with information on GHG emissions from highly polluting plants for the period 2007–2016 at the metropolitan region level in the European Union and the UK. Results show that the local development of environmental technologies reduces GHG emissions, while the local development of digital technologies increases them, albeit in the latter case different technologies seem to have different impacts on the environment, with big data and computing infrastructures being the most detrimental. We also find differential impacts across regions depending on local endowment levels of the respective technologies: the beneficial effect of environmental technologies is stronger in regions with large digital technology endowments and, conversely, the detrimental effect of digital technologies is weaker in regions with large green technology endowments. Policy actions promoting the “twin” transition should take this evidence into account, in light of the potential downside of the digital transformation when not combined with the green transformation.
Journal Article
Convergence and concentration of empirical measures under Wasserstein distance in unbounded functional spaces
We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization can cover Euclidean spaces with large dimensionality, with the optimal dependence on the dimensionality. Our method also covers the important case of Gaussian processes in separable Hilbert spaces, with rate-optimal upper bounds for functional data distributions whose coordinates decay geometrically or polynomially. Moreover, our bounds of the expected value can be combined with mean-concentration results to yield improved exponential tail probability bounds for the Wasserstein error of empirical measures under Bernstein-type or log Sobolev-type conditions.
Journal Article
Markups and Firm-Level Export Status
In this paper, we develop a method to estimate markups using plantlevel production data. Our approach relies on cost-minimizing producers and the existence of at least one variable input of production. The suggested empirical framework relies on the estimation of a production function and provides estimates of plant-level markups without specifying how firms compete in the product market. We rely on our method to explore the relationship between markups and export behavior. We find that markups are estimated significantly higher when controlling f or unobserved productivity; that exporters charge, on average, higher markups and that markups increase upon export entry.
Journal Article
One Swallow Doesn't Make a Summer: New Evidence on Anchoring Effects
Some researchers have argued that anchoring in economic valuations casts doubt on the assumption of consistent and stable preferences. We present new evidence that explores the strength of certain anchoring results. We then present a theoretical framework that provides insights into why we should be cautious of initial empirical findings in general. The model importantly highlights that the rate of false positives depends not only on the observed significance level, but also on statistical power, research priors, and the number of scholars exploring the question. Importantly, a few independent replications dramatically increase the chances that the original finding is true.
Journal Article
DISTRIBUTED ESTIMATION OF PRINCIPAL EIGENSPACES
Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts latent principal factors that contribute to the most variation of the data. When data are stored across multiple machines, however, communication cost can prohibit the computation of PCA in a central location and distributed algorithms for PCA are thus needed. This paper proposes and studies a distributed PCA algorithm: each node machine computes the top K eigenvectors and transmits them to the central server; the central server then aggregates the information from all the node machines and conducts a PCA based on the aggregated information. We investigate the bias and variance for the resulting distributed estimator of the top K eigenvectors. In particular, we show that for distributions with symmetric innovation, the empirical top eigenspaces are unbiased, and hence the distributed PCA is “unbiased.” We derive the rate of convergence for distributed PCA estimators, which depends explicitly on the effective rank of covariance, eigengap, and the number of machines. We show that when the number of machines is not unreasonably large, the distributed PCA performs as well as the whole sample PCA, even without full access of whole data. The theoretical results are verified by an extensive simulation study. We also extend our analysis to the heterogeneous case where the population covariance matrices are different across local machines but share similar top eigenstructures.
Journal Article
Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging
The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.
Journal Article
A Comparison of Machine Learning and Empirical Approaches for Deriving Bathymetry from Multispectral Imagery
Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.
Journal Article