Skewed Statistics Research Articles

Breaking Barriers and Navigating the Future of Emergency Care: The Role of the Emergency Nurse Practitioner.

Over the past two decades, the emergency nurse practitioner (ENP) role has significantly enhanced emergency care in the United States. With the increasing complexity of emergency care, ENPs have expanded into nontraditional settings, necessitating regulatory updates to maintain patient safety. The Consensus Model for Advanced Practice Registered Nurse Regulation has influenced ENP practice, yet consistent regulatory recognition is still needed due to the marked evolution in ENP practice. Despite collaborative efforts to prepare the ENP workforce and ensure competency through standardized education, certification, and licensure, recent unethical publications and skewed statistics have threatened the specialty's progress. Addressing these challenges is crucial for advancing the ENP role and ensuring high-quality emergency care delivery.

Black American History and Culture: Untold, Reframed, Stigmatized and Fetishized to the Point of Global Ethnocide

Purpose: A poetic work of fiction haunts the base of the Statue of Liberty. The act overshadowed the original tribute to the Civil War victory and the Emancipation Proclamation. Abraham Lincoln's praises of the Black American military fell silent. Eurocentrists shrouded centuries of genius and scaled-down Black American mastery. Sagas of barrier-breaking Olympians, military heroes, Wild West pioneers, and inventors ended as forgotten footnotes. Today, countries around the world fetishize Black American history and culture to the point of ethnocide. The real-time case study of Woni Spotts explores the phenomenon. Until ancient traditions evolve with authenticity, global cultures will wither and die. The presented research chronicles over half a millennium of archives. Lists with names, dates, and genealogies seal the Black American legacy in stone.
 Methodology: The presented research for case studies draws from archival data, dated events, news articles, and an interview with Woni Spotts. The case studies generated three lists. Fifty sports and competitions were dated and cataloged. The athletes were analyzed by a genealogist. Forty music and dance genres were cataloged by publishing or recording dates. The artists were analyzed by a genealogist. Copyright infringements were noted. Inventors were researched for U.S. patents. NASA astronauts and inventors were analyzed by a genealogist.
 Findings: The presented research showed centuries of untold, reframed, stigmatized, and fetishized Black American history and culture. In the case studies, foreigners of African descent (Africans, Caribbeans, Central Americans, and South Americans) practiced ethnocidal behavior in concert with European descendants. Prolific abolitionists, patriots, politicians, and inventors were written out of history. Superstar athletes were obstructed or outshined by fictional
 Recommendations: Case studies showed centuries of fragmented narratives created biases and distortions. Black Americans were written out of history, reframed as background characters, stigmatized with skewed statistics, and fetishized globally to the point of ethnocide. The presented research stands as a vital resource for preservationists. Music and dance genre architects were solidified by publishing and recording dates. Athletic events, inventions, and NASA scientists were recorded.

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery.

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed "subjective inflation." Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., "variance misperception"). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors-but used them incorrectly-showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.

Statistical Anomalies Associated with NBA Scorekeepers: A Bootstrap Investigation

In 2009, a Deadspin article documented how an anonymous NBA scorekeeper skewed statistics for various purposes. This research sets out to test the assertions set forth by the author of the Deadspin piece and uses prospect theory and unique statistical techniques to do so. The findings support the assertions proposed in the Deadspin piece that scorekeepers for various venues are skewing the statistics. The discussion provides suggestions for correcting this bias as well as demonstrates how asymmetric incentives by scorekeepers in favor of the home teams could be misused in determining salaries, playing time, trade negotiations, and media attention.

Relative Entropy and Minimum-Variance Pricing Kernel in Asset Pricing Model Evaluation.

Recent literature shows that many testing procedures used to evaluate asset pricing models result in spurious rejection probabilities. Model misspecification, the strong factor structure of test assets, or skewed test statistics largely explain this. In this paper we use the relative entropy of pricing kernels to provide an alternative framework for testing asset pricing models. Building on the fact that the law of one price guarantees the existence of a valid pricing kernel, we study the relationship between the mean-variance efficiency of a model’s factor-mimicking portfolio, as measured by the cross-sectional generalized least squares (GLS) statistic, and the relative entropy of the pricing kernel, as determined by the Kullback–Leibler divergence. In this regard, we suggest an entropy-based decomposition that accurately captures the divergence between the factor-mimicking portfolio and the minimum-variance pricing kernel resulting from the Hansen-Jagannathan bound. Our results show that, although GLS statistics and relative entropy are strongly correlated, the relative entropy approach allows us to explicitly decompose the explanatory power of the model into two components, namely, the relative entropy of the pricing kernel and that corresponding to its correlation with asset returns. This makes the relative entropy a versatile tool for designing robust tests in asset pricing.

Statistical Phase Transitions and Extreme Events in Shallow Water Waves with an Abrupt Depth Change

Predicting extreme events in complex nonlinear systems is an extremely challenging and important area in climate science. Important examples include extreme events near coastal continental shelves for shallow and deep water waves. Recent laboratory experiments reveal a remarkable transition from near Gaussian to highly skewed anomalous statistics with extreme events by measuring the surface water wave displacements in shallow water across an abrupt depth change (ADC). A statistical dynamical model has been proposed and used to accurately predict the representative statistical transition using Gibbs invariant measures for the truncated KdV equation at low and high inverse temperatures representing flows before and after the ADC. In this paper, we use much lower dimensional truncated paradigm models to understand the statistical phase transition and the creation of extreme events. Especially, a two-mode interacting model with exact integrable dynamics is adopted to characterize the core transition mechanism as the model parameter varies. The choice of the radically truncated two-mode model is motivated by the self-similar solution structures with reducing numbers of truncated modes. A clear separation of distinct dynamics in the phase space is discovered for the Gibbs ensembles sampled from different inverse temperatures. Direct numerical tests with various model truncation sizes are presented to illustrate the statistical transition in parameter regimes. The analysis here can also provide a theoretical guideline for a wider variety of models concerning the generation of extreme events and anomalous statistics.

Influence of higher-order stimulated Brillouin scattering on the occurrence of extreme events in self-pulsing fiber lasers

We investigate the dynamical behavior of a self-pulsing laser under the influence of stimulated Brillouin scattering (SBS), a system which has previously been shown to favor extreme statistics. Using a laser model coupling a multi-Stokes Brillouin scattering process with the population inversion formalism for the gain and taking into account saturable absorption effects, we demonstrate that different statistical distribution types arise as the nonlinear interactions between the laser and higher-order SBS waves lead to the occurrence of high intensity short pulses. By taking into account up to five Stokes orders, we show that highly skewed statistics and pulses with extreme peak intensities can be obtained, allowing us to describe more accurately the experimental observations and to better apprehend the underlying physics. We also unexpectedly demonstrate that the acoustic noise does not affect the emergence of such extreme events.

Story Unspoken: An Analysis of Maternity Benefits Amendment Act, 2017 and Its Implications on the Modern Industrial Discourse

The developing societies have changed considerably in the past decade. In this decadal growth which has positively affected the economic and social structures in place, women have played a considerable role. One cannot overlook the contribution of women in building up the skilled labour workforce in the country even though the statistics show a massively skewed gender ratio in the labour workforce. Developing societies need to comprehend this crucial role and provide leverage to integrate the gender-centric role of women with that of men. In furtherance of this objective, the concept of social justice has evolved in recent times. This concept has emanated from the judicial interpretation of legislation which is enacted to serve communities in need of support and care. One such forward-thinking legislation for the labour industry is the Maternity Benefits Act, 1961 (along with its recent amendment of 2017) which is borne out of the fact that the women in labour workforce are in need of protection in delicate situations like pregnancy so that they are not harassed in the name of work commitments. To complement such laws, the judiciary has played an equally important role and judgments in cases like B. Shah v. Labour Court, Combaitore show this role in an effective manner but still a lot of lacunas and complications remains. To achieve a holistic development and improve upon these skewed statistics, there is a need to identify the mitigating factors in the objective of achieving social equality for women. This research paper is an endeavour to study upon these changing landscapes of women empowerment in the country through the lens of judicial interpretations and an analytical study of the amended legislation of 2017.

On the Limits of CALIOP for Constraining Modeled Free Tropospheric Aerosol

AbstractThe spaceborne Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument provides valuable information on the vertical distribution of global aerosol and is often used to evaluate vertical aerosol distributions in general circulation models (GCMs). Here we show, however, that the detection limit of the CALIOP retrievals mean background aerosol is not detected, leading to substantially skewed statistics that moreover differ significantly by product. In the CALIOP Level 2 product this missing low‐backscatter aerosol results in the retrieved aerosol distribution significantly overrepresenting aerosol backscatter and extinction in the middle and upper troposphere if taken to be representative of the undetected aerosol. The CALIOP Level 3 product assumes no aerosol where none is detected, which then leads to an underestimation in the aerosol extinction profile in the upper troposphere. Using the ECHAM‐HAM GCM, we estimate that the mean fraction of aerosol undetected by CALIOP daytime (nighttime) retrievals is 41% (44%) globally.

Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques

Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs.

Simulating Dispersion in the Evening-Transition Boundary Layer

We investigate dispersion in the evening-transition boundary layer using large-eddy simulation (LES). In the LES, a particle model traces pollutant paths using a combination of the resolved flow velocities and a random displacement model to represent subgrid-scale motions. The LES is forced with both a sudden switch-off of the surface heat flux and also a more gradual observed evolution. The LES shows ‘lofting’ of plumes from near-surface releases in the pre-transition convective boundary layer; it also shows the subsequent ‘trapping’ of releases in the post-transition near-surface stable boundary layer and residual layer above. Given the paucity of observations for pollution dispersion in evening transitions, the LES proves a useful reference. We then use the LES to test and improve a one-dimensional Lagrangian Stochastic Model (LSM) such as is often used in practical dispersion studies. The LSM used here includes both time-varying and skewed turbulence statistics. It is forced with the vertical velocity variance, skewness and dissipation from the LES for particle releases at various heights and times in the evening transition. The LSM plume spreads are significantly larger than those from the LES in the post-transition stable boundary-layer trapping regime. The forcing from the LES was thus insufficient to constrain the plume evolution, and inclusion of the significant stratification effects was required. In the so-called modified LSM, a correction to the vertical velocity variance was included to represent the effect of stable stratification and the consequent presence of wave-like motions. The modified LSM shows improved trapping of particles in the post-transition stable boundary layer.

Image statistics and the perception of surface qualities

The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.