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The design of personalized movement training and rehabilitation pipelines relies on the ability of assessing the activation of individual muscles concurrently with the resulting joint torques exerted during functional movements. Despite advances in motion capturing, force sensing and bio-electrical recording technologies, the estimation of muscle activation and resulting force still relies on lengthy experimental and computational procedures that are not clinically viable. This work proposes a wearable technology for the rapid, yet quantitative, assessment of musculoskeletal function. It comprises of (1) a soft leg garment sensorized with 64 uniformly distributed electromyography (EMG) electrodes, (2) an algorithm that automatically groups electrodes into seven muscle-specific clusters, and (3) a EMG-driven musculoskeletal model that estimates the resulting force and torque produced about the ankle joint sagittal plane. Our results show the ability of the proposed technology to automatically select a sub-set of muscle-specific electrodes that enabled accurate estimation of muscle excitations and resulting joint torques across a large range of biomechanically diverse movements, underlying different excitation patterns, in a group of eight healthy individuals. This may substantially decrease time needed for localization of muscle sites and electrode placement procedures, thereby facilitating applicability of EMG-driven modelling pipelines in standard clinical protocols.

The autonomous vehicle (AV) industry works very hard to create public trust in both AV technology and its developers. Building trust is part of a strategy to permit the industry itself to manage the testing and deployment of self-driving vehicle technology without regulatory interference. The AV industry supplements its appeal for trust by extolling a plethora of public benefits that AV technology supposedly will bring to society. In this article, we examine the behavior of certain AV industry participants in various concrete situations, considering whether actions by these AV industry participants promote trust or, conversely, create concerns. To evaluate whether a particular action promotes trust, we consider both common sense intuitions as well as recommendations contained in three publications with intended global applicability: (i) the recently published IEEE Standard Model Process for Addressing Ethical Concerns during System Design (IEEE 7000); (ii) Ethics Guidelines for Trustworthy AI; and (iii) Policy and Investment Recommendations for Trustworthy AI. These publications inform our analysis of practices that merit trust (and helps us identify those that do not). Our analysis identifies certain trust destroying practices, suggesting that the public should not accept either AV industry appeals for trust or take at face value the proffered narrative of benefits. We perceive a need for more regulation by federal, state and local governments. Despite our concerns, we discuss how the AV industry might earn trust by changing its adversarial approach to law, regulation and disclosure, starting by compliance with standards promulgated by the engineering community. AV industry participants should shift to cooperation with lawmakers and regulators by embracing engineering standards relating to safety, such as SAE J3018, and ethical principles which promote trust, such as identified in IEEE 7000. SAE J3016 should be replaced as a regulatory standard by a simpler rubric which promotes safety with less complexity. Cooperation might take place by use of negotiated rulemaking.

We propose a multiplicative dynamic factor structure for the conditional modelling of the variances of an N-dimensional vector of financial returns. We identify common and idiosyncratic conditional volatility factors. The econometric framework is based on an observation-driven time series model that is simple and parsimonious. The common factor is modeled by a normal density and is robust to fat-tailed returns as it averages information over the cross-section of the observed N-dimensional vector of returns. The idiosyncratic factors are designed to capture the erratic shocks in returns and therefore rely on fat-tailed densities. Our model is potentially of a high-dimension, is parsimonious and it does not necessarily suffer from the curse of dimensionality. The relatively simple structure of the model leads to simple computations for the estimation of parameters and signal extraction of factors. We derive the stochastic properties of our proposed dynamic factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the reliability of our method to track the common conditional volatility factor are investigated by means of a Monte Carlo study. Finally, we illustrate our approach with two empirical studies. The first study is for a panel of financial returns from ten stocks of the S&P100. The second study is for the panel of returns from all S&P100 stocks.

We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global cross-sectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network structures. Model selection, filtering of the dynamic factors, and estimation are carried out iteratively using a new algorithm that combines the Expectation-Maximization algorithm with coordinate descent and gradient descent, allowing us to efficiently maximize an l1- and l2-penalized state space likelihood function. A Monte Carlo simulation study illustrates the good performance of the algorithm in terms of determining the presence and magnitude of global and/or local cross-sectional dependence. In an empirical application, we investigate monthly US interest rate data on 15 maturities over almost 40 years. We find that besides a changing number of global dynamic factors, there is heterogeneous local dependence among neighboring maturities. Taking this heterogeneity into account substantially improves out-of-sample forecasting performance.

Background: We evaluated the time to progression and survival outcomes of second-line therapy for metastatic colorectal cancer among adults aged 70 years and older compared to adults aged less than 70 years following progression on first-line clinical trials. Methods: Associations between clinical and disease characteristics, time to initial progression and rate of receipt of second-line therapy were evaluated. Time to progression and overall survival were compared between older and younger adults enrolled on second-line trials by Cox regression, adjusting for age, sex, ECOG PS, number of metastatic sites and presence of metastasis in the lung, liver or peritoneum. Findings: Older adults comprised 16·4% of enrolled patients on first-line trials. Older adults and those with ECOG PS >0 were less likely to receive second-line therapy than younger adults. Odds of receiving second-line therapy decreased by 11% for each additional decade of life in multivariate analysis (p-value = 0·0117). Older and younger adults enrolled in second-line trials experience similar median time to progression and median overall survival (5·1 vs. 5·2 months; 11·6 vs. 12·4 months, respectively). Interpretation: Older adults are less likely to receive second-line therapy for metastatic colorectal cancer, though we did not observe a statistical difference in survival outcomes for older vs younger adults following second-line therapy. Further study is needed to examine factors, including patient preference, along with the effects of comorbidities and functional status impacting decisions to treat older adults with second-line therapy. Inclusion of geriatric assessment may provide more objective criteria regarding the risks and benefits of second-line therapy to aid decision making for older adults considering second-line therapy for metastatic colorectal cancer. Funding: The NCI GCC Spore Career Development Award (5P50CA127003-08) funded Dr. McCleary’s effort. The ARCAD Foundation funded data collection and analysis. Declaration of Interest: Alberto F Sobrero served on advisory boards and as a speaker at satellite symposia for Merck, Roche, Servier, Incyten, Sanofi, Bayer, Bristol Myers Squibb, Amgen, Eli Lilly, and Pierre Fabre. James M Cleary received research funding to his institution from Abbvie, Merus, Roche, and Bristol Myers Squib. He received research funding from Merck, Astrazeneca, Esperas Pharma, and Tesaro, received consulting fees from Bristol Myers Squibb, and received travel funding from Bristol Myers Squib. Volker Heinemann served as honoraria for Merck, Amgen, Roche, Sanofi, Sirtex, Servier, Pfizer. He served as a consultant or Merck, Amgen, Roche, Sanofi, Sirtex, and Bristol Myers Squibb. He served on an advisory board for Merck, Novartis, Boehringer Ingelheim, Servier, Pierre-Fabre, Celgene, and Terumo. He received research funding to his institution from Merck, Amgen, Roche, Sanofi, Pfizer, Boehringer-Ingelheim, Sirtex, Bayer, and Servier. He received travel funding from Merck, Roche, Amgen, Sirtex, Bayer, and Servier. Stuart M Lichtman is supported by the NCI Cancer Center Support Grant (P30CA008748). All other author have nothing to declare. Ethical Approval: All analyses were performed with approval from the local Institutional Review Board in accordance with the precepts of the Helsinki Declaration.

Statistics Netherlands uses a state space model to estimate the Dutch unemployment by using monthly series about the labour force surveys (LFS). More accurate estimates of this variable can be obtained by including auxiliary information in the model, such as the univariate administrative series of claimant counts. Legislative changes and economic crises may affect the relation between survey-based and auxiliary series. This time-changing relationship is captured by a time-varying correlation parameter in the covariance matrix of the transition equation’s error terms. We treat the latter parameter as a state variable, which makes the state space model become nonlinear and therefore its estimation by Kalman filtering and maximum likelihood infeasible. We therefore propose an indirect inference approach to estimate the static parameters of the model, which employs cubic splines for the auxiliary model, and a bootstrap filter method to estimate the time-varying correlation together with the other state variables of the model. We conduct a Monte Carlo simulation study that shows that our proposed methodology is able to correctly estimate both the time-constant parameters and the state vector of the model. Empirically we find that the financial crisis of 2008 triggered a deeper and more prolonged deviation between the survey-based and the claimant counts series, than a legislative change in 2015. Promptly tackling such changes, which our proposed method does, results in more realistic real-time unemployment estimates.

We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature ending of the 2019/2020 football season has occurred due to the COVID-19 pandemic. We propose a model-based method as a possible alternative to the use of the incomplete standings to determine the final table. This method measures the performance of the teams in the matches of the season that have been played and predicts the remaining non-played matches through a paired-comparison model. The main advantage of the method compared to the incomplete standings is that it takes account of the bias in the performance measure due to the schedule of the matches in a season. Therefore, the resulting ranking of the teams based on our proposed method can be regarded as more fair in this respect. A forecasting study based on historical data of seven of the main European competitions is used to validate the method. The empirical results suggest that the model-based approach produces more accurate predictions of the true final standings than those based on the incomplete standings.

We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not only for the 10-days-ahead horizon required by the Basel Committee but even for long horizons, like one-month or one-year-ahead. The latter has recently attracted considerable attention due to the different properties of short term risk and long run risk. The key insight behind our importance sampling based approach is the sequential construction of marginal and conditional importance densities for consecutive periods. We report substantial accuracy gains for all the considered horizons in empirical studies on two datasets of daily financial returns, including a highly volatile period of the recent financial crisis. To illustrate the flexibility of the proposed construction method, we present how it can be adjusted to the frequentist case, for which we provide counterparts of both Bayesian applications.

A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region of interest in case of misspecification. The first main contribution of the paper is the novel concept of the Partially Censored Posterior (PCP), where the set of model parameters is partitioned into two subsets: for the first subset of parameters we consider the standard marginal posterior, for the second subset of parameters (that are particularly related to the region of interest) we consider the conditional censored posterior. The censoring means that observations outside the region of interest are censored: for those observations only the probability of being outside the region of interest matters. This quasi-Bayesian approach yields more precise parameter estimation than a fully censored posterior for all parameters, and has more focus on the region of interest than a standard Bayesian approach. The second main contribution is that we introduce two novel methods for computationally efficient simulation: Conditional MitISEM, a Markov chain Monte Carlo method to simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is introduced to further decrease the numerical standard errors of the Value-at-Risk and Expected Shortfall estimators. The third main contribution is that we consider the effect of using a time-varying boundary of the region of interest, which may provide more information about the left tail of the distribution of the standardized innovations. Extensive simulation and empirical studies show the ability of the introduced method to outperform standard approaches.

Since its creation in 1934, the Federal Communications Commission has had broad discretion to approve or deny the consummation of major transactions by wireless and wired communications firms under a public interest standard. Despite the passage of decades, neither the FCC nor the courts have put meaningful limits on what the FCC can pursue under this authority. Today, regulated companies--including broadcast TV and radio, satellite TV and radio, cable TV, and Internet service providers--are the primary producers and distributors of mass media and publications. We model the FCC’s decisions to extract concessions from firms under the empire building model. The FCC’s ad hoc determinations of the public interest and the impracticability of timely judicial review has pernicious effects on modern media and the rule of law. Consistent with the model, the FCC increasingly extracts nominally voluntary concessions from firms--including programming decisions, hiring practices, and “net neutrality” compliance--in the transaction approvals that the agency is legally barred from or unwilling to pursue through the normal regulatory process. We argue that the FCC’s unpredictable and coercive transaction reviews violate rule of law norms and pose significant First Amendment problems. We question the necessity of the FCC’s powerful merger review authority and suggest that if Congress does not revoke the agency’s authority, the agency should at least promulgate guidelines for what its public interest standard requires.

We propose a basic high-dimensional dynamic model for tennis match results with time varying player-specific abilities for different court surface types. Our statistical model can be treated in a likelihood-based analysis and is capable of handling high-dimensional datasets while the number of parameters remains small. In particular, we analyze 17 years of tennis matches for a panel of over 500 players, which leads to more than 2000 dynamic strength levels. We find that time varying player-specific abilities for different court surfaces are of key importance for analyzing tennis matches. We further consider several other extensions including player-specific explanatory variables and the accountance of specific configurations for Grand Slam tournaments. The estimation results can be used to construct rankings of players for different court surface types. We finally show that our proposed model can also be effective in forecasting. We provide evidence that our model significantly outperforms existing models in the forecasting of tennis match results.

In January 2017, the Supreme Court of the United States declined to hear a case brought by Flytenow, a flight-sharing startup, against the Federal Aviation Administration (FAA). While Flytenow’s legal challenge ended when the Supreme Court refused to hear the case, on policy grounds the company continues to have the better argument. Ultimately, the flight-sharing industry was shut down because the FAA can define common carriage expansively in its guidance and interpret that definition without oversight. Congress should intervene by explicitly defining common carriage narrowly in statute. This paper discusses private pilots’ traditional right to share costs and why such cost-sharing matters for general aviation. Next, the paper shows that Flytenow tried to comply with this traditional framework and previous interpretations of aviation law, and it discusses the company’s legal travails. There is a possible remedy: Congress could unwind the FAA’s confused flight-sharing rulings by simply defining common carrier more narrowly in statute. The FAA’s current definition undermines the purpose of common carriage as conceived under the common law. This paper proposes some elements that Congress should consider as it formulates an appropriate definition. Finally, the paper addresses some objections.

Certificate-of-need (CON) laws disallow hospitals, nursing homes, ambulatory surgical centers (ASCs), and other healthcare providers from entering new markets, expanding their practice, or making certain capital investments without first receiving approval from state regulators. These laws are currently in effect in 36 states. Over the past 40 years, CON laws have been justified as a way to achieve numerous public policy goals, such as controlling costs, increasing charity care, and protecting access to health care in rural communities by shielding hospitals from increased competition. However, the effects of CON laws on rural health care are not well understood. We examine the effect of entry regulation on ASCs and community hospitals and find that there are both more rural hospitals and more rural ambulatory surgical centers per capita in states without a CON program regulating the opening of an ASC. This finding indicates that CON laws may not be protecting access to rural health care, but are instead correlated with decreases in rural access.

We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated maximum likelihood estimation method based on importance sampling and assess its performance in a Monte Carlo study. This modelling framework with trend, seasonal and irregular components is applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility pattern for the trend component can be associated with the energy shocks in the 1970s while that for the irregular component responds to the monetary regime changes from the 1980s. The scale of the changes in the seasonal component has been largest during the beginning of the 1990s. We finally present empirical evidence of relative improvements in the accuracies of point and density forecasts for monthly US inflation.

The cost of higher education in the United States has risen dramatically in recent years. Numerous explanations have been provided to explain this increase. This paper focuses on one contributing factor: The dramatic growth in the size and expense of non-academic administrators and other university bureaucrats, which has outpaced the growth of expenditures on academic programs. Given that university faculty are typically viewed as the constituency that primarily controls universities, this growth of non-academic employees and expenses appears to be anomalous. Some theories are provided to explain this transition.