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Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.

Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it significantly outperforms the benchmark asymptotic approximation in finite samples, especially for tail quantiles such as Value‐at‐Risk (VaR). We use the proposed new test to study the VaR and CoVaR (Adrian and Brunnermeier (2016)) of a collection of US financial institutions.

Most patients have successful outcomes with minimal limitations after distal biceps repair, but a minority continues experiencing functional constraints. We hypothesize that low scores on a validated mental health measure correlate with worse patient-reported outcomes and increased difficulty with return to work and sport.

There is growing interest in the problem of burnout among physicians. Here, we examine the factors associated with burnout in orthopedic surgical training.

The best screening method for developmental dysplasia of the hip is controversial. Ultrasonography is sensitive, but cost-effectiveness may limit its use. This study assessed whether ultrasound screening would increase in effectiveness if targeted toward infants with established risk factors for developmental dysplasia of the hip and normal findings on physical examination. All ultrasound scans performed at the authors’ institution from January 2007 through January 2011 to screen for developmental dysplasia of the hip were reviewed. Infants with risk factors for developmental dysplasia of the hip and normal findings on physical examination by orthopedic faculty or a pediatrician were selected. Of the 530 cases that were reviewed, 217 had risk factors for developmental dysplasia of the hip and normal findings on physical examination. Mean age of the 217 selected patients was 6.9 weeks. Of the patients, 83% were female, 77% had breech presentation, 30% were firstborn children, 13% had intrauterine packaging abnormalities, and 3% had a family history of developmental dysplasia of the hip. Of the 217 infants, 44 had 1 risk factor, 121 had 2 risk factors, 46 had 3 risk factors, and 6 had 4 risk factors. Dynamic ultrasound evaluation showed instability in 17 patients, for a 7.8% incidence of developmental dysplasia of the hip. All 17 patients were treated with a Pavlik harness. The results suggested that selective ultrasound screening may be effective in infants with risk factors and normal findings on physical examination. Selective ultrasound screening changed treatment management in almost 8% of patients and clinical follow-up in 6.5%. Analysis of the cost-effectiveness of screening is needed. [The best screening method for developmental dysplasia of the hip is controversial. Ultrasonography is sensitive, but cost-effectiveness may limit its use. This study assessed whether ultrasound screening would increase in effectiveness if targeted toward infants with established risk factors for developmental dysplasia of the hip and normal findings on physical examination. All ultrasound scans performed at the authors’ institution from January 2007 through January 2011 to screen for developmental dysplasia of the hip were reviewed. Infants with risk factors for developmental dysplasia of the hip and normal findings on physical examination by orthopedic faculty or a pediatrician were selected. Of the 530 cases that were reviewed, 217 had risk factors for developmental dysplasia of the hip and normal findings on physical examination. Mean age of the 217 selected patients was 6.9 weeks. Of the patients, 83% were female, 77% had breech presentation, 30% were firstborn children, 13% had intrauterine packaging abnormalities, and 3% had a family history of developmental dysplasia of the hip. Of the 217 infants, 44 had 1 risk factor, 121 had 2 risk factors, 46 had 3 risk factors, and 6 had 4 risk factors. Dynamic ultrasound evaluation showed instability in 17 patients, for a 7.8% incidence of developmental dysplasia of the hip. All 17 patients were treated with a Pavlik harness. The results suggested that selective ultrasound screening may be effective in infants with risk factors and normal findings on physical examination. Selective ultrasound screening changed treatment management in almost 8% of patients and clinical follow-up in 6.5%. Analysis of the cost-effectiveness of screening is needed. [Orthopedics.2015; 38(8):e722–e726.]

We construct a simple measure of the aggregate illiquidity of hedge fund portfolios, based on the cross-sectional average first-order autocorrelation coefficient of hedge fund returns, and show that it has strong and robust in- and out-of-sample forecasting power for 72 portfolios of international equities, U.S. corporate bonds, and currencies over the 1994 to 2013 period. The forecasting ability of hedge fund illiquidity for asset returns is in most cases greater than, and provides independent information relative to, well-known predictive variables. We rationalize these findings using a simple equilibrium model, in which hedge funds provide liquidity in asset markets.

The article presents the poem "The Secret Heart," by Andrew Patton. First Line: Across the years he could recall; Last Line: To know that hands held up the sun.

Recent studies in the empirical finance literature have reported evidence of two types of asymmetries in the joint distribution of stock returns. The first is skewness in the distribution of individual stock returns. The second is an asymmetry in the dependence between stocks: stock returns appear to be more highly correlated during market downturns than during market upturns. In this article we examine the economic and statistical significance of these asymmetries for asset allocation decisions in an out-of-sample setting. We consider the problem of a constant relative risk aversion (CRRA) investor allocating wealth between the risk-free asset, a small-cap portfolio, and a large-cap portfolio. We use models that can capture time-varying moments up to the fourth order, and we use copula theory to construct models of the time-varying dependence structure that allow for different dependence during bear markets than bull markets. The importance of these two asymmetries for asset allocation is assessed by comparing the performance of a portfolio based on a normal distribution model with a portfolio based on a more flexible distribution model. For investors with no short-sales constraints, we find that knowledge of higher moments and asymmetric dependence leads to gains that are economically significant and statistically significant in some cases. For short sales-constrained investors the gains are limited.