Statistical Background Research Articles

A CREATIVE JOURNEY INTO BIG DATA ANALYSTS

A big data analyst is a professional who specializes in analyzing and interpreting large and complex sets of data. They use various tools and techniques to extract insights and trends from the data, which can help businesses make informed decisions and improve their operations.To become a big data analyst, you typically need a strong background in statistics, computer science, and data analysis. You may also need experience working with big data tools such as Hadoop, Spark, and NoSQL databases. Additionally, strong communication and collaboration skills are important, as big data analysts often work with teams of other data professionals and stakeholders within the organization.

The NANOGrav 15 yr Data Set: Evidence for a Gravitational-wave Background

We report multiple lines of evidence for a stochastic signal that is correlated among 67 pulsars from the 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves. The correlations follow the Hellings–Downs pattern expected for a stochastic gravitational-wave background. The presence of such a gravitational-wave background with a power-law spectrum is favored over a model with only independent pulsar noises with a Bayes factor in excess of 1014, and this same model is favored over an uncorrelated common power-law spectrum model with Bayes factors of 200–1000, depending on spectral modeling choices. We have built a statistical background distribution for the latter Bayes factors using a method that removes interpulsar correlations from our data set, finding p = 10−3 (≈3σ) for the observed Bayes factors in the null no-correlation scenario. A frequentist test statistic built directly as a weighted sum of interpulsar correlations yields p = 5 × 10−5 to 1.9 × 10−4 (≈3.5σ–4σ). Assuming a fiducial f −2/3 characteristic strain spectrum, as appropriate for an ensemble of binary supermassive black hole inspirals, the strain amplitude is (median + 90% credible interval) at a reference frequency of 1 yr−1. The inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from a population of supermassive black hole binaries, although more exotic cosmological and astrophysical sources cannot be excluded. The observation of Hellings–Downs correlations points to the gravitational-wave origin of this signal.

En första kriminologisk inblick i lagen (2007:1150) om tillsyn över hundar och katter

AbstractWe present a first criminological insight into the Act (2007:1150) on the supervision of dogs and cats in terms of reporting and prosecution statistics, against the background of statistics on authority decisions based on this law, appeals of authority decisions and statistics from Folksam and the central dog register of the Swedish Board of Agriculture. The number of prosecutions under the Supervision Act in the district courts is 30-46 prosecutions per year between 2004 and 2021. Women account for 65% of dog ownership and for 42% of offenses against the Supervision Act. The proportion of reports received that led to a decision by the County Administrative Boards varied from zero to approx. 30% in 2020 and 2021. Of the 13 persons who died due to dog-related injuries 1998-2021 seven were women, in the age groups 45 and older, while of 6 180 patients who required inpatient care for dog-related injuries 2001-2021 made up 648 children of 0-4 years old.Introduction of the current Supervision Act in 2008 resulted in an increased load on administrative courts, while criminal cases in the district courts have been at about the same level as before. Transfer of responsibility for the Supervision Act from the Swedish Police Authority to the County Administrative Boards in 2018 increased the workload of the administrative courts even more. The criminal cases that are settled in the district courts no longer deal with first-time incidents where bite-prone dogs have caused property or personal damage or considerable inconvenience, but recurrences where society already had intervened through authority decisions, reflecting ineffectiveness of authority decisions based on the law. Not to criminalize Section 1 of the Supervision Act and transfer of responsibility for the law from the Swedish Police Authority to the County Admi- ESSAY nistrative boards have reduced the burden on the Police Authority and the district courts at the expense of an increased burden on the County Administrative boards and the administrative courts. The question is what, from a socio-economic perspective, is the best solution to approach the problem of dangerous dogs.

On the statistical background of quantum mechanics: generalities and a concrete example

We revisit our description of randomness in quantum processes that began in collaboration of Jean Ginibre. The calculations were performed on a worked example: the fluorescence of a single two-level atom pumped by a resonant laser field. This pump laser is described classically (by a function, not an operator). Our aim is first to built a Kolmogorov-type equation (K-equation) for the atomic state, so that the two parameters θ, φ that define this density matrix are random functions of time, therefore the atomic density matrix is a random density matrix. Such an approach, initiated for gas kinetics, was not yet applied to quantum phenomena, whereas it is especially tailored to very quick events well separated (in time) like the quantum jumps observed in spontaneous emission of photons by an atom. Here, we try to clarify the basis of our statistical approach leading to the K-equation below, and we present the main results deduced from it. We explain finally that our approach can be interpreted in terms of Everett’s theory of many-worlds, because at every emission a new history begins for the atom, with two nonoverlapping wave functions.

G:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).

g:Profiler is a reliable and up-to-date functional enrichment analysis tool that supports various evidence types, identifier types and organisms. The toolset integrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings. g:Profiler provides multiple programmatic interfaces to access its functionality. These can be easily integrated into custom workflows and external tools, making them valuable resources for researchers who want to develop their own solutions. g:Profiler has been available since 2007 and is used to analyse millions of queries. Research reproducibility and transparency are achieved by maintaining working versions of all past database releases since 2015. g:Profiler supports 849 species, including vertebrates, plants, fungi, insects and parasites, and can analyse any organism through user-uploaded custom annotation files. In this update article, we introduce a novel filtering method highlighting Gene Ontology driver terms, accompanied by new graph visualizations providing a broader context for significant Gene Ontology terms. As a leading enrichment analysis and gene list interoperability service, g:Profiler offers a valuable resource for genetics, biology and medical researchers. It is freely accessible at https://biit.cs.ut.ee/gprofiler.

A Statistical Analysis for Intensity Wavelength-Resolution SAR Difference Images

This paper presents a statistical analysis of intensity wavelength-resolution synthetic aperture radar (SAR) difference images. In this analysis, Anderson Darling goodness-of-fit tests are performed, considering two different statistical distributions as candidates for modeling the clutter-plus-noise, i.e., the background statistics. The results show that the Gamma distribution is a good fit for the background of the tested SAR images, especially when compared with the Exponential distribution. Based on the results of this statistical analysis, a change detection application for the detection of concealed targets is presented. The adequate selection of the background distribution allows for the evaluated change detection method to achieve a better performance in terms of probability of detection and false alarm rate, even when compared with competitive performance change detection methods in the literature. For instance, in an experimental evaluation considering a data set obtained by the Coherent All Radio Band Sensing (CARABAS) II UWB SAR system, the evaluated change detection method reached a detection probability of 0.981 for a false alarm rate of 1/km2.

Introducing Machine Learning in Auditing Courses

ABSTRACT The advances in machine learning have gained close attention from audit practitioners and standard setters. However, fewer than half of accounting programs teach predictive analysis, including machine learning. To develop students’ knowledge and skills of machine learning in auditing applications, this study introduces machine learning to the accounting curriculum and presents a novel hands-on approach for teaching machine learning in auditing courses. The objective is to provide students who have no statistics background and programming skills with the basic knowledge of machine learning and hands-on exercises for predicting auditing tasks. In addition to instruction manuals, this study demonstrates an implementation of machine learning exercises in a graduate-level auditing course.

Writing statistical methods for ecologists

AbstractTheMethodssection is a key component of any ecology research publication containing detailed information on how the data were collected and analyzed. However, descriptions of which statistical methods were used and how they were applied can substantially vary and may not provide enough information for the analyses to be reproducible. Computational and statistical programming advances have allowed ecological researchers without a strong statistical or mathematical background to access and use increasingly complex statistical methods. Thus, statistical methods are written by and need to be accessible to researchers across a range of quantitative expertise. Poorly writtenMethodssections can incorrectly inflate the strength of or call into question the results of an analysis. Although there are resources available, we have not found one that is specific to writing statistical methods, includes all the elements we discuss, and is targeted for ecologists. Here we provide guidelines for ecological researchers when writing statistical methods and review frequent errors made inStatistical Methodssections. We highlight some common dos and don'ts when writingStatistical Methodssections and present a simple checklist to help guide authors with their writing to ensure reproducibility. We illustrate the use of this guidance with two examples.

How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

GRRIEn Analysis: A Data Science Cheat Sheet for Earth Scientists Learning from Global Earth Observations

Abstract Globally available environmental observations (EOs), specifically from satellites and coupled Earth system models, represent some of the largest datasets of the digital age. As the volume of global EOs continues to grow, so does the potential of these data to help Earth scientists discover trends and patterns in Earth systems at large spatial scales. To leverage global EOs for scientific insight, Earth scientists need targeted and accessible exposure to skills in reproducible scientific computing and spatiotemporal data science, and to be empowered to apply their domain understanding to interpret data-driven models for knowledge discovery. The Generalizable, Reproducible, Robust, and Interpreted Environmental (GRRIEn) analysis framework was developed to prepare Earth scientists with an introductory statistics background and limited/no understanding of programming and computational methods to use global EOs to successfully generalize insights from local/regional field measurements across unsampled times and locations. GRRIEn analysis is generalizable, meaning results from a sample are translated to landscape scales by combining direct environmental measurements with global EOs using supervised machine learning; robust, meaning that the model shows good performance on data with scale-dependent feature and observation dependence; reproducible, based on a standard repository structure so that other scientists can quickly and easily replicate the analysis with a few computational tools; and interpreted, meaning that Earth scientists apply domain expertise to ensure that model parameters reflect a physically plausible diagnosis of the environmental system. This tutorial presents standard steps for achieving GRRIEn analysis by combining conventions of rigor in traditional experimental design with the open-science movement. Significance Statement Earth science researchers in the digital age are often tasked with pioneering big data analyses, yet have limited formal training in statistics and computational methods such as databasing or computer programming. Earth science researchers often spend tremendous amounts of time learning core computational skills, and making core analytical mistakes, in the process of bridging this training gap, at risk to the reputability of observational geostatistical research. The GRRIEn analytical framework is a practical guide introducing community standards for each phase of the computational research pipeline (dataset engineering, model training, and model diagnostics) to promote rigorous, accessible use of global EOs in Earth systems research.

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

Traffic queue length at intersection is one of the most important measures of traffic signal performance and the traffic signal optimization. The spread of the ride-hailing services makes GPS data widely available with large area coverage, allowing us to quantify the queue length in a wide area without the need for additional installation cost like conventional sensors. Hence, queue length can be observed simultaneously and the relationship between intersections can be captured. Due to the current myriad of available queue length data, an analysis tool is needed to turn the data into useful knowledge to support traffic operations and management. However, the current queue length visualization only focuses on a real-time fashion, which cannot provide information about the change of the queue or its uncertainty. Such information is crucial for understanding the probability of the queue to be spillover, as well as determining the lengths of turning lanes to minimize blockage. In this research, we developed a spatial uncertainty-aware visualization by applying a conventional technique called box plot to visualize queue lengths on a map derived from taxi GPS data. A prototype of a web mapping application has been implemented and tested with users. The results indicated that participants with a statistics background have an advantage over those without a statistical background to understand the box plot. However, all of them were able to identify the uncertainty of the queue lengths as well as to compare the uncertainty between intersections and times.

Ultrasonic multi-view data merging using the vector coherence factor

Over the years, ultrasonic imaging, both in the context of medical and industrial applications, has ushered in the development of many advanced techniques, some based on the use of the phase of the measured signal instead of its amplitude. These techniques use what is called coherence factors to enhance image quality. Leveraging these factors when calculating the image provides an effective solution to the low restitution of small scatterers, where specular reflectors in conventional ultrasonic imaging are strongly restituted, by weighting an amplitude-based beamformer output. In the field of nondestructive testing, this solution generally boils down to multiplying the well-known Total Focusing Method (TFM) image by a coherence factor map which is also obtained through delay-and-sum (DAS). It has also been demonstrated that coherence factor maps, especially in the case of the vector coherence factor (VCF), can be used as interpretable images themselves, which then results in an amplitude-free imaging process with many potential advantages. Additionally, the fusion of multi-view (or multi-mode) TFM images is still of particular interest since the burden of selecting a single or several views for an inspection scenario highly depends on the geometry and position of the defect that needs to be identified. Although sensitivity maps have been introduced to help in the selection of relevant views, no similar tool currently exists for views generated using directly coherence factor maps. In this study, two different merging approaches for multi-view VCF images are explored and evaluated: pixel-by-pixel summation and maximum pixel value selection. A probabilistic threshold, derived from the VCF statistical background, is also defined to enhance the resulting merged view contrast. The images obtained through the proposed methods were evaluated both qualitatively and quantitatively using the Contrast-to-Noise Ratio (CNR). Experiments were conducted on two sets of samples. The first one was a 20 mm thick steel plate with Flat Bottom Holes (FBH) machined on its edge mimicking reflectors used for calibration of a circumferential weld inspection apparatus. The second one was composed of two 19.05 mm (34′′) thick steel plates with EDM notches at several angles centered at the mid-wall or surface-breaking on the backwall. Since the experimental setup, consisting of a 60-element probe, with a center frequency of 7.5 MHz, standing on a 36°Rexolite wedge, involved exciting mostly shear waves, only views composed of the transversal wave paths were considered for merging. Results show that by applying either one of the proposed merging methods, all artificial defects are reconstructed with a satisfactory CNR (above 30 dB), irrespective of the shape or location of the defects. Overall, the pixel-by-pixel summation and the maximum pixel value methods gave similar results. Applying the proposed threshold on the merged view improved the CNR by up to 15.8 dB. Finally, these simple but generic and robust multi-view merging methods and their associated thresholds could be employed while using a simplified hardware setup, as demonstrated previously in the literature.

Development of Spatial Uncertainty-Aware Visualization of Historical Traffic Queue Length

Traffic queue length at intersection is one of the most important measures of traffic signal performance and the traffic signal optimization. The spread of the ride-hailing services makes GPS data widely available with large area coverage, allowing us to quantify the queue length in a wide area without the need for additional installation cost like conventional sensors. Hence, queue length can be observed simultaneously and the relationship between intersections can be captured. Due to the current myriad of available queue length data, an analysis tool is needed to turn the data into useful knowledge to support traffic operations and management. However, the current queue length visualization only focuses on a real-time fashion, which cannot provide information about the change of the queue or its uncertainty. Such information is crucial for understanding the probability of the queue to be spillover, as well as determining the lengths of turning lanes to minimize blockage. In this research, we developed a spatial uncertainty-aware visualization by applying a conventional technique called box plot to visualize queue lengths on a map derived from taxi GPS data. A prototype of a web mapping application has been implemented and tested with users. The results indicated that participants with a statistics background have an advantage over those without a statistical background to understand the box plot. However, all of them were able to identify the uncertainty of the queue lengths as well as to compare the uncertainty between intersections and times.

The satellite population around luminous red galaxies in the 25 deg2 DESI Legacy Imaging Surveys Early Data Release

ABSTRACT Luminous red galaxies, or LRGs, are representative of the most massive galaxies and were originally selected in the Sloan Digital Sky Survey as good tracers of large-scale structure. They are dominated by by uniformly old stellar populations, have low star formation rates, early type morphologies, and little cold gas. Despite having old stellar populations and little in situ star formation, studies have shown that they have grown their stellar mass since z = 1, implying that they grow predominantly via the accretion of satellites. Tests of this picture have been limited because of the lack of deep imaging data sets that both covers a large enough area of the sky to contain substantial numbers of LRGs and that also is deep enough to detect faint satellites. We use the 25 deg2 Early Data Release (EDR) of the DESI Legacy Imaging Surveys to characterize the satellite galaxy population of LRGs out to z = 0.65. The DESI Legacy Imaging Surveys are comprised of grz imaging to 2–2.5 mag deeper than SDSS and with better image quality. We use a new statistical background technique to identify excess populations of putative satellite galaxies around 1823 LRGs at 0.2 &amp;lt; z &amp;lt; 0.65. In three redshift and luminosity bins we measure the numbers of satellite galaxies and their r−z colour distribution down to rest-frame g-band luminosity limits at least 3.6 times fainter than L*. In addition, we develop a forward modeling technique and apply it to constrain the mean number of satellites in each of our redshift and luminosity bins. Finally, we use these estimates to determine the amount of stellar mass growth in LRGs down to the local Universe.

Do national culture and board attributes influence corporate investment decisions?

Purpose The purpose of this paper is to study the relationship between board attributes and national culture on firms’ investment decisions. Design/methodology/approach The study used data from listed firms from seven Sub-Saharan Africa countries. Descriptive analysis was performed to provide the background statistics of the variables examined. This was followed by regression analysis, which constitutes the main data analysis. Findings The multiple regression analysis results indicate a negative relationship between uncertainty avoidance (UAI) and corporate investment decisions. The study also found that there is a negative relationship between the interaction between UAI and the number of independent board members and corporate investment decisions. Originality/value This study is one of the few to measure the influence of governance variables and national culture on corporate investment decisions in Sub-Sahara Africa.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).

Point-Of-Care Ultra-Sound (POCUS) for suspected surgical abdominal conditions in rural India-a mixed-method health needs assessment.

Evidence has shown a trend towards rising mortality from surgical abdominal conditions with increasing distance from hospitals in Eastern India. It has been claimed that imaging modalities such as portable ultrasound are ideal for use in rural areas of developing countries for diagnosis and management of a variety of acute abdominal conditions. This mixed-methods study aimed to explore the need for increased access to POCUS in rural, hard-to-reach populations within India, and to identify barriers that may exist to increasing this access. This mixed-methods study utilized a health needs assessment framework. Quantitative data was extracted from medical records within two outreach camp organisations supporting hard-to-reach populations in Uttarakhand and Jharkhand to provide contextual background statistics. Qualitative data was collected using semi-structured interviews taken from employees of each outreach camp. A framework analysis was employed to analyse the qualitative data. Both datasets were analysed in parallel. Twelve semi-structured interviews were undertaken, and 3 months of medical records were reviewed. Themes that presented themselves from the data included the use of camps for case-finding of conditions amenable to routine surgery, a need for POCUS to help with this diagnostic process, a perceived high disease burden from renal calculi, and complex cultural and legislative barriers to POCUS specific to this setting. POCUS was considered a useful tool in expanding access to surgery for these settings and finding ways of overcoming cultural and legislative barriers to its use should be of high priority. POCUS should be highly portable, robust, easily repairable, and battery operated. For telemedicine to be fully utilised, signal quality will have to be proven to be reliable.

Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This is applied greatly in business enterprises and government sectors for delivering the services to their customers, clients and citizens. But, the interaction is successful only based on the trust that each device has on another. Thus trust is very much essential for a social network. As Internet of Things have access over sensitive information, it urges to many threats that lead data management to risk. This issue is addressed by trust management that help to take decision about trustworthiness of requestor and provider before communication and sharing. Several trust-based systems are existing for different domain using Dynamic weight method, Fuzzy classification, Bayes inference and very few Regression analysis for IoT. The proposed algorithm is based on Logistic Regression, which provide strong statistical background to trust prediction. To make our stand strong on regression support to trust, we have compared the performance with equivalent sound Bayes analysis using Beta distribution. The performance is studied in simulated IoT setup with Quality of Service (QoS) and Social parameters for the nodes. The proposed model performs better in terms of various metrics. An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services. The most salient features of IoT system is to design it with scalability, extendibility, compatibility and resiliency against attack. The existing works finds a way to integrate direct and indirect trust to converge quickly and estimate the bias due to attacks in addition to the above features.

A New Approach for 4-D Exospheric Tomography Based on Optimal Interpolation and Gaussian Markov Random Fields

Exospheric tomography is a computational 3-D imaging technique that provides the estimates of the neutral density distributions of the terrestrial exosphere from space-based ultraviolet (UV) measurements. Variability of neutral densities during geomagnetically active conditions has been previously reported, motivating the development of time-dependent tomographic techniques that can characterize both the spatial and temporal scales of densities during these events. However, solving the dynamic exospheric tomography problem can be challenging owing to its ill-posedness. In this letter, we introduce a novel algorithm for 4-D exospheric tomography based on optimal interpolation (OI) and Gaussian Markov random field (GMRF) theory. The OI analysis enables iterative reconstructions of the exosphere when a statistical background field is provided. Its mean is selected from previous knowledge of the exosphere, and its covariance matrix is estimated using GMRF. To validate the performance, we apply our proposed methodology to six days of UV data acquired by National Aeronautics and Space Administration (NASA) two-wide angle imaging neutral-atom spectrometer (TWINS) mission during the geomagnetic storm that occurred on June 15, 2008.

The art of translational statistics

AbstractTranslational statistics report results of an analysis of data in a way that is designed to facilitate the use of the results as a guide in practical settings. In this sense, they go beyond the basic inferential statistics that are the focus of most applications of statistics. The goal is to reach a target audience whose concern is how to implement results based on what has been learned. This audience may not have the statistical background to understand the fine details of the analysis, but it is essential that they understand what the results mean. We illustrate the fundamental ideas with several examples from a variety of settings.