Statistical Background Research Articles

Search for significant clusters in high throughput sequencing data

A new, statistically based method of clustering previously used by authors for analysis of frequency and point data and as well for searching clusters in samples and graphs is applied to the task of searching clusters in high throughput sequence data. Such problem often arises in bioinformatics in attempt to reveal different forms may be presented in one nosology. Nevertheless methods existing in literature have no sufficient statistical background and the question of significance for revealed clusters usually is not considered. Complications due different sequencing deep while construction the library and big biological variations in gene expression are common. We propose method for discovering statistically significant clusters based on ranks of relative frequencies and nonparametric criteria to overcome these obstacles. Such approach allows avoiding dependence from different sequence deep, knowledge of exact form for distribution of gene expression and hypothesis that all expressions have the same functional form.

Real-time motion detection with candidate masks and region growing for moving cameras

A fast and reliable motion detection method is proposed for moving cameras. Existing methods generally build background models and then apply background subtraction for moving pixels detection. In background modeling, statistical methods and/or features, such as edges, local binary patterns are used. Parameters of the proposed methods need to be updated adaptively for better performance. In recent studies, adaptive learning rates are used with main and candidate background models. Simple and adaptive thresholding methods are used for foreground detection in different studies. We build a statistical background model with hue–saturation–value color space and perform background subtraction with the contribution of optical flow vectors. Dense optical flow is applied between warped the background model and the current frame to estimate optical flow vectors. If a pixel has a larger magnitude, we set a weight value for each pixel in the background subtraction process. Then, we extract two candidate foreground masks and apply some postprocessing techniques to determine the final mask. Our proposed method works fast in real time, and the experimental results on two different datasets show that our approach has the highest performance compared with the methods in the literature.

Integrated reporting disclosure in Malaysia: regulations and practice

PurposeIntegrated reporting (IR) is a new trend in corporate reporting that has spread rapidly in recent years for disclosing financial and non-financial information. This study aims to assess the status of the current regulations and the trends in IR disclosure practice in an emerging market, Malaysia, by providing a comparative analysis of the IR disclosure level (IRDL) and IR disclosure quality (IRDQ).Design/methodology/approachThe current study has developed a comprehensive IR disclosure index based on the international integrated reporting framework (IIRF), which comprises 100 items divided into four categories (background, assurance and reliability, content and form). The data were collected from annual reports of companies listed on the Bursa Malaysia over the three years 2017 to 2019, based on 267 observations. Content analysis technique was used to evaluate and measure IRDL and IRDQ. Descriptive analysis was performed to provide the background statistics of the variables examined.FindingsIR regulations are at an early stage, and IR adoption is still voluntary in the Malaysian market. Only 267 Malaysian company-year observations during the years 2017–2019 have adopted IR techniques. However, descriptive analysis results showed that Malaysian companies have moved towards the preparation of IR consistent with the IIRF. The findings indicate a significant increase in both IRDL and IRDQ over this period, after the recent recommendation by the Malaysian code of corporate governance (2017) on adopting IR. Further, the results show statistically significant differences in the mean of IRDL and IRDQ between large and small companies.Practical implicationsThese results are important for regulators and policymakers in articulating new IR legislation in an emerging market and for corporate entities and investors in shaping their understanding of IR disclosure practice in the Malaysian institutional context.Originality/valueTo the best of the researchers’ knowledge, the study is among the first to address the IR regulation status and practice in Malaysian companies. It also established a comprehensive index for measuring IRDL and IRDQ based on the IIRF. The results add to the meagre descriptive literature on IR practice by providing comprehensive insights into IR practice from the perspective of an emerging country.

Use of Propensity Scoring and Its Application to Real-World Data: Advantages, Disadvantages, and Methodological Objectives Explained to Researchers Without Using Mathematical Equations.

Real-time data collection of patient health status and medications is sped up with modern electronic devices and technologies. As real-world data provide enormous research opportunities, propensity score (PS) methods have been getting attention due to their theoretical grounds in a nonrandomized study setting. In contrast to randomized clinical trials, observational clinical data obtained from a real-world database may not have balanced distributions of patient characteristics between treatment and control groups at the beginning of the respective study. These imbalanced distributions may cause a bias in an estimated treatment effect, which needs to be eliminated. Propensity scoring is one class of statistical methods to address the imbalance issue of real-world data sets. This article provides basic concepts and assesses advantages, disadvantages, and methodological objectives of propensity scoring. Targeting clinical pharmacology researchers with limited statistical background, 5 representative methods are reviewed and visualized: matching, stratification, covariate modeling, inverse probability of treatment weighting, and doubly robust methods. Examples of applications of PS methods were selected from the literature of outcomes research and drug development, nephrology, and pediatrics. Opportunities of applications related to these examples are described. Furthermore, potential future applications of PS methods in clinical pharmacology are discussed. The 21st Century Cures Act signed in 2016 encourages scientists to find opportunities to apply propensity scoring to real-world data. This article underscores that scientists need to justify their choice of statistical methods, whether a PS method or an alternative method, based on their clinical study design, statistical assumptions, and research objectives.

Synthetic galaxy clusters and observations based on Dark Energy Survey Year 3 Data

ABSTRACT We develop a novel data-driven method for generating synthetic optical observations of galaxy clusters. In cluster weak lensing, the interplay between analysis choices and systematic effects related to source galaxy selection, shape measurement, and photometric redshift estimation can be best characterized in end-to-end tests going from mock observations to recovered cluster masses. To create such test scenarios, we measure and model the photometric properties of galaxy clusters and their sky environments from the Dark Energy Survey Year 3 (DES Y3) data in two bins of cluster richness $\lambda \in [30; 45)$, $\lambda \in [45; 60)$ and three bins in cluster redshift ($z\in [0.3; 0.35)$, $z\in [0.45; 0.5)$ and $z\in [0.6; 0.65)$. Using deep-field imaging data, we extrapolate galaxy populations beyond the limiting magnitude of DES Y3 and calculate the properties of cluster member galaxies via statistical background subtraction. We construct mock galaxy clusters as random draws from a distribution function, and render mock clusters and line-of-sight catalogues into synthetic images in the same format as actual survey observations. Synthetic galaxy clusters are generated from real observational data, and thus are independent from the assumptions inherent to cosmological simulations. The recipe can be straightforwardly modified to incorporate extra information, and correct for survey incompleteness. New realizations of synthetic clusters can be created at minimal cost, which will allow future analyses to generate the large number of images needed to characterize systematic uncertainties in cluster mass measurements.

Make sense of the research: A primer for educational leaders

Education leaders know that they should use research when choosing interventions for their schools, but they don’t always know how to read the research that is available. David Steiner explains some of the reasons that reading research is a low priority for educators on the front lines and offers some guidance for determining whether research results are meaningful without an extensive background in statistics. Ideally, education decision makers should look for randomized control trials with high effect sizes and low p-values.

Using the likelihood ratio in bloodstain pattern analysis

There is an apparent paradox that the likelihood ratio (LR) approach is an appropriate measure of the weight of evidence when forensic findings have to be evaluated in court, while it is typically not used by bloodstain pattern analysis (BPA) experts. This commentary evaluates how the scope and methods of BPA relate to several types of evaluative propositions and methods to which LRs are applicable. As a result of this evaluation, we show how specificities in scope (BPA being about activities rather than source identification), gaps in the underlying science base, and the reliance on a wide range of methods render the use of LRs in BPA more complex than in some other forensic disciplines. Three directions are identified for BPA research and training, which would facilitate and widen the use of LRs: research in the underlying physics; the development of a culture of data sharing; and the development of training material on the required statistical background. An example of how recent fluid dynamics research in BPA can lead to the use of LR is provided. We conclude that an LR framework is fully applicable to BPA, provided methodic efforts and significant developments occur along the three outlined directions.

Source apportionment and natural background levels of major ions in shallow groundwater using multivariate statistical method: A case study in Huaibei Plain, China

Understanding the sources, natural background levels (NBLs), and threshold values (TVs) of the major ions in groundwater is essential for the effective protection of water resources. In this study, a total of 70 shallow groundwater samples were collected in Suzhou, Huaibei Plain, China. A variety of statistical methods and cumulative probability distribution techniques were performed to identify the sources, NBLs, and TVs of the major ions. The major ion concentrations found in decreasing order as follows: HCO3− > SO42− > NO3− > Cl− and Na+ > Ca2+ > Mg2+. Piper diagram for hydrochemical types shows that groundwater types were Mg–HCO3 (36%), Ca–HCO3 (34%), and Na–HCO3 (30%). According to the factor and the Unmix model analysis, anthropogenic (agriculture-related) and geogenic source (water–rock interactions-related) were identified to be responsible for the chemical composition of the groundwater in the study area, and their mean contributions for the major ion concentrations are 47.9% and 52.1%, respectively. The NBLs for Na+, Ca2+, Mg2+, Cl−, SO42−, and NO3− were determined to be 29.5–44.2, 26.2–38.9, 18.9–39.5, 1.0–9.9, 12.9–19.4, and 2.1–16.5 mg/L, respectively, and the TVs were calculated as 122.1, 169.5, 39.5, 129.6, 134.7, and 18.3 mg/L, respectively. Moreover, this study shows the feasibility and reliability of using these multivariate statistical methods and natural background levels to evaluate the status of groundwater quality.

From Divining Rods to Statistics: A Forensic Analysis of the Misuse of Statistics in the Estimation of Environmental Impact

Shortly after my retirement, I became involved in local water issues and learned that I could be of especial use in work on those issues because of my background in statistics. What I am reporting here is a minimally-technical account of the experience I had using statistics as a forensic tool to unearth what appears to have been a misuse of statistics in the modeling of the environmental impact of a proposed desalination project to provide water for the community where I reside. Of potential interest to statisticians is how statistics can be useful in detective work.

Pre-Seismic Irregularities during the 2020 Samos (Greece) Earthquake (M = 6.9) as Investigated from Multi-Parameter Approach by Ground and Space-Based Techniques

We present a comprehensive analysis of pre-seismic anomalies as computed from the ground and space-based techniques during the recent Samos earthquake in Greece on 30 October 2020, with a magnitude M = 6.9. We proceed with a multi-parametric approach where pre-seismic irregularities are investigated in the stratosphere, ionosphere, and magnetosphere. We use the convenient methods of acoustics and electromagnetic channels of the Lithosphere–Atmosphere–Ionosphere-Coupling (LAIC) mechanism by investigating the Atmospheric Gravity Wave (AGW), magnetic field, electron density, Total Electron Content (TEC), and the energetic particle precipitation in the inner radiation belt. We incorporate two ground-based IGS GPS stations DYNG (Greece) and IZMI (Turkey) for computing the TEC and observed a significant enhancement in daily TEC variation around one week before the earthquake. For the space-based observation, we use multiple parameters as recorded from Low Earth Orbit (LEO) satellites. For the AGW, we use the SABER/TIMED satellite data and compute the potential energy of stratospheric AGW by using the atmospheric temperature profile. It is found that the maximum potential energy of such AGW is observed around six days before the earthquake. Similar AGW is also observed by the method of wavelet analysis in the fluctuation in TEC values. We observe significant energetic particle precipitation in the inner radiation belt over the earthquake epicenter due to the conventional concept of an ionospheric-magnetospheric coupling mechanism by using an NOAA satellite. We first eliminate the particle count rate (CR) due to possible geomagnetic storms and South Atlantic Anomaly (SAA) by the proper choice of magnetic field B values. After the removal of the statistical background CRs, we observe a significant enhancement of CR four and ten days before the mainshock. We use Swarm satellite outcomes to check the magnetic field and electron density profile over a region of earthquake preparation. We observe a significant enhancement in electron density one day before the earthquake. The parameters studied here show an overall pre-seismic anomaly from a duration of ten days to one day before the earthquake.

Evaluating tests for diagnosing COVID-19 in the absence of a reliable reference standard: pitfalls and potential solutions

Evaluating tests for diagnosing COVID-19 in the absence of a reliable reference standard: pitfalls and potential solutions

The GOGREEN survey: dependence of galaxy properties on halo mass at z > 1 and implications for environmental quenching

ABSTRACT We use photometric redshifts and statistical background subtraction to measure stellar mass functions in galaxy group-mass (4.5−8 × 1013 M⊙) haloes at 1 < z < 1.5. Groups are selected from COSMOS and SXDF, based on X-ray imaging and sparse spectroscopy. Stellar mass (Mstellar) functions are computed for quiescent and star-forming galaxies separately, based on their rest-frame UVJ colours. From these we compute the quiescent fraction and quiescent fraction excess (QFE) relative to the field as a function of Mstellar. QFE increases with Mstellar, similar to more massive clusters at 1 < z < 1.5. This contrasts with the apparent separability of Mstellar and environmental factors on galaxy quiescent fractions at z ∼ 0. We then compare our results with higher mass clusters at 1 < z < 1.5 and lower redshifts. We find a strong QFE dependence on halo mass at fixed Mstellar; well fit by a logarithmic slope of d(QFE)/dlog (Mhalo) ∼ 0.24 ± 0.04 for all Mstellar and redshift bins. This dependence is in remarkably good qualitative agreement with the hydrodynamic simulation bahamas, but contradicts the observed dependence of QFE on Mstellar. We interpret the results using two toy models: one where a time delay until rapid (instantaneous) quenching begins upon accretion to the main progenitor (‘no pre-processing’) and one where it starts upon first becoming a satellite (‘pre-processing’). Delay times appear to be halo mass-dependent, with a significantly stronger dependence required without pre-processing. We conclude that our results support models in which environmental quenching begins in low-mass (<1014M⊙) haloes at z > 1.

On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model

Restricted Boltzmann machines (RBMs) with a binary visible layer of size N and a Gaussian hidden layer of size P have been proved to be equivalent to a Hopfield neural network (HNN) made of N binary neurons and storing P patterns ξ, as long as the weights w in the former are identified with the patterns. Here we aim to leverage this equivalence to find effective initialisations for weights in the RBM when what is available is a set of noisy examples of each pattern, aiming to translate statistical mechanics background available for HNN to the study of RBM’s learning and retrieval abilities. In particular, given a set of definite, structureless patterns we build a sample of blurred examples and prove that the initialisation where w corresponds to the empirical average ξ¯ over the sample is a fixed point under stochastic gradient descent. Further, as a toy application of the duality between HNN and RBM, we consider the simplest random auto-encoder (a three layer network made of two RBMs coupled by their hidden layer) and evidence that, as long as the parameter setting corresponds to the retrieval region of the dual HNN, reconstruction and denoising can be accomplished trivially, while when the system is in the spin-glass phase inference algorithms are necessary. This questions the need for larger retrieval regions which we obtain by applying a Gram–Schmidt orthogonalisation to the patterns: in fact, this procedure yields to a set of patterns devoid of correlations and for which the largest retrieval region can be accomplished. Finally we consider an application of duality also in a structured case: we test this approach on the MNIST dataset, and obtain that the network performs already ∼67% of successful classifications, suggesting it can be exploited as a computationally-cheap pre-training.

Neyman–Pearson Criterion-Based Change Detection Methods for Wavelength–Resolution SAR Image Stacks

This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman–Pearson criterion. The first proposed method uses the data from wavelength–resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods.

Higher incidence of COVID-19 in patients with adrenal insufficiency compared with background statistics

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

The Basis for Reliability-Based Mechanical Properties of Structural Aluminium Alloys

Adequate knowledge of mechanical properties and their statistical description is the basis for performing reliable verification of design methods and design of structures in general. The probabilistic design approach implemented in Eurocodes requires statistical data on all variables used in the design procedure. Although aluminium was introduced in structural Eurocodes more than four decades ago (ENV), the statistical database of mechanical properties is still inadequate. To provide a reliable statistical background, data collection was performed concerning aluminium products mainly found in the European market, within the last 20 years regarding certificates from the aluminium industry and 30 years regarding data from the research community. The collected data include aluminium alloy series 1xxx, 5xxx, 6xxx, and 7xxx, mainly extruded, and relevant mechanical properties such as 0.2% proof strength, ultimate strength, Young’s modulus, and Poisson’s ratio. They were fit to distributions, and relevant fractiles were determined, along with an analysis of nominal to characteristic and design value ratios. Variation of ratios obtained shows that that the majority of nominal values are economical and reliable. However, certain adjustments to nominal values are required to achieve a uniform reliability level in terms of the choice of alloy and temper.

Cross-scene foreground segmentation with supervised and unsupervised model communication

In this paper11Dong Liang, Bin Kang and Xinyu Liu contributed equally to this work., we investigate cross-scene video foreground segmentation via supervised and unsupervised model communication. Traditional unsupervised background subtraction methods often face the challenging problem of updating the statistical background model online. In contrast, supervised foreground segmentation methods, such as those that are based on deep learning, rely on large amounts of training data, thereby limiting their cross-scene performance. Our method leverages segmented masks from a cross-scene trained deep model (spatio-temporal attention model (STAM), pyramid scene parsing network (PSPNet), or DeepLabV3+) to seed online updates for the statistical background model (CPB), thereby refining the foreground segmentation. More flexible than methods that require scene-specific training and more data-efficient than unsupervised models, our method outperforms state-of-the-art approaches on CDNet2014, WallFlower, and LIMU according to our experimental results. The proposed framework can be integrated into a video surveillance system in a plug-and-play form to realize cross-scene foreground segmentation.

A uniformly most powerful detector of gas plumes against a cluttered background

The agriculture, waste management, and energy sectors make major contributions of methane to the atmosphere. Cost-effective means of locating both anthropogenic and natural sources of methane and other gases are needed in order to improve understanding of greenhouse gas budgets and facilitate mitigation. One approach uses imaging spectrometers operating on remote sensing platforms, such as aircraft or satellites, to locate specific types of plumes. This paper develops spectral algorithms for the detection of any absorptive gas, for example, methane in the shortwave infrared region. The presence of a target gas is inferred from its known absorptivity spectrum along with local background statistics gathered by the sensor. The problem of distinguishing the target signal from clutter is usually solved by modeling the spectral background with some member of the class of elliptical distributions, most commonly a multivariate Gaussian. That class of models is replaced here by the lognormal distribution. A logarithmic transformation then recasts the associated hypothesis test into a standard form, allowing derivation of a uniformly most powerful solution. It is theoretically optimal simultaneously for all gas concentrations. This leads further to new algorithms for the detection and quantification of gas mixtures.

Robust Centralized CFAR Detection for Multistatic Sonar Systems

This paper proposes a novel centralized Constant false alarm ratio (CFAR) detector for multistatic sonar systems. The detector employs the idea of Variability index (Ⅵ) CFAR detection, to adaptively select the matched detection algorithm in diversified undersea environments. All the echo data from mutistatic sonar receivers are transmitted into the centralized fusion center. Firstly, the background statistics of reference cells from different nodes are analyzed. Then choose one appropriate centralized detection algorithm according to the background statistics, which refers to the centralized Cell averaging CFAR (CA-CFAR), greatest of CFAR, order statistic CFAR detection algorithms. The performance of the proposed detector is analyzed by computer simulation and measured sonar data. The results show that, compared to the centralized CA-CFAR detector, the introduced centralized detector achieves a better robustness in multiply heterogeneous undersea environments.

Assessing Sensitivity of MERRA-2 to AMSU-A in the Upper Stratosphere

AbstractMicrowave temperature sounders provide key observations in data assimilation, both in the current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) are assimilated beginning with NOAA-15 and continuing through the current period. The time series of observation minus background statistics for AMSU-A channels sensitive to the upper stratosphere and lower mesosphere show variabilities due to changes in the AMSU-A constellation in the early AMSU-A period. Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites. This effort characterizes the sensitivity, both in terms of the observations and the MERRA-2 data. Furthermore, it explores the use of reprocessed and intercalibrated datasets to evaluate whether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this interplatform sensitivity, which can be mitigated by using reprocessed data. The results explore the importance of the reprocessing of the AMSU-A radiances as well as their intercalibration.