SPATIAL RESOLUTION ENHANCEMENT OF OVERSAMPLED IMAGES USING REGRESSION DECOMPOSITION AND SYNTHESIS

A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.

A Systematic Review of the Impact of COVID-19 on Global Sporting Events in 2020: The Tokyo 2020 Summer Olympics

The Corona virus disease 2019 (COVID-19), like no other pandemic has taken the world by storm, affecting all and any spheres of life. This effect has also impacted global sporting events such as the 2020 Summer Olympics that were scheduled for the 24th of July 2020 to the 9th of July 2020 in Tokyo, Japan. Historically, the Summer Olympics have been cancelled 3 times due to war but the postponement that occurred in 2020 is unprecedented. The socio-economic implications are still yet to be fully explored and realised. The purpose of this research is to therefore examine the impact of COVID-19 on the Tokyo 2020 Summer Olympics. The study will adopt a systematic literature review of material on the COVID-19 pandemic in relation to sporting events and statistical inferences will be conducted based on publicly accessible secondary data sources. Considering that the pandemic is still an ongoing phenomenon the findings and analysis cannot be conclusive, a snapshot based on current data and scientific predictions will be provided on what COVID-19 meant to global sporting events. A broad analysis of the pandemic’s impact on sport will be provided despite the focus being on the Tokyo 2020 Summer Olympics. Last, this study serves as a template for further research on COVID-19’s impact on sporting events in general, preferably studies conducted post the pandemic for reflection purposes based on more conclusive data.

Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples

The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.

Why p-values are not measures of evidence

The recommendations by Muff and colleagues are an incoherent approach to statistical inferences, and should only be used if one wants to signal a misunderstanding of p-values. Coherent alternatives to quantify evidence exist, such as likelihoods and Bayes factors. Therefore, researchers should not follow the recommendation by Muff and colleagues to report p = 0.08 as ‘weak evidence’, p = 0.03 as ‘moderate evidence’, and p = 0.168 as ‘no evidence’.

Statistical Inferences and Analysis based on the COVID-19 data from the United States

This paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. Using empirical data analysis and statistical inference tools, we bring out several exciting and important aspects of the pandemic, otherwise hidden. Specific patterns seen in demo-graphics such as race/ethnicity and age are discussed both qualitatively and quantitatively. We also study the role played by factors such as population density. Connections between COVID-19 and other respiratory diseases are also covered in detail. The temporal dynamics of the COVID-19 outbreak and the impact of vaccines in controlling the pandemic are also looked at with sufficient rigor. It is hoped that statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy preparation and thus adequately preparing, should a similar situation arise in the future.

Standardizing workflows in imaging transcriptomics with the abagen toolbox

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as p ≥ 1:0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

Learning the statistics of pain: computational and neural mechanisms

Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.

Women, Younger Clinicians’, and Caregivers’ Experiences of Burnout and Well-being During COVID-19 in a US Healthcare System

Background The COVID-19 pandemic brought rapid changes to the work and personal lives of clinicians. Objective To assess clinician burnout and well-being during the COVID-19 pandemic and guide healthcare system improvement efforts. Design A survey asking about clinician burnout, well-being, and work experiences. Participants Surveys distributed to 8141 clinicians from June to August 2020 in 9 medical groups and 17 hospitals at Sutter Health, a large healthcare system in Northern California. Main Measures Burnout was the primary outcome, and other indicators of well-being and work experience were also measured. Descriptive statistics and multivariate logistic regression analyses were performed. All statistical inferences were based on weighted estimates adjusting for response bias. Key Results A total of 3176 clinicians (39.0%) responded to the survey. Weighted results showed 29.2% reported burnout, and burnout was more common among women than among men (39.0% vs. 22.7%, p<0.01). In multivariate models, being a woman was associated with increased odds of reporting burnout (OR=2.19, 95% CI: 1.51–3.17) and being 55+ years old with lower odds (OR=0.54, 95% CI: 0.34–0.87). More women than men reported that childcare/caregiving was impacting work (32.9% vs. 19.0%, p<0.01). Even after controlling for age and gender, clinicians who reported childcare/caregiving responsibilities impacted their work had substantially higher odds of reporting burnout (OR=2.19, 95% CI: 1.54–3.11). Other factors associated with higher burnout included worrying about safety at work, being given additional work tasks, concern about losing one’s job, and working in emergency medicine or radiology. Protective factors included believing one’s concerns will be acted upon and feeling highly valued. Conclusions This large survey found the pandemic disproportionally impacted women, younger clinicians, and those whose caregiving responsibilities impacted their work. These results highlight the need for a holistic and targeted strategy for improving clinician well-being that addresses the needs of women, younger clinicians, and those with caregiving responsibilities.

Recent Developments in Factor Models and Applications in Econometric Learning

This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.