Statistical Aspects Research Articles

Quantitative Structure-Property Relationship Analysis on Priority PAHs Using Certain Closed Neighbourhood Topological Indices

Molecular structures and their physicochemical properties are related harmoniously. Having the potential uses in our everyday life, graph theory has become one of the most iconic and discussed areas of applied mathematics. Chemical graph theory (CGT) is a branch of graph theory that incorporates chemical aspects. The topological index (TI) is a well-defined graph-theoretical tool that offers a mathematical relationship with molecular structures and also characterizes their topology. TI has real-time applications such as isomer discrimination, drug design, QSPR, and QSAR studies in various domains of chemistry, including nanotechnology and biochemistry. Herein, seven closed neighborhood TIs are investigated. Using QSPR regression analysis, the chemical signature of the indices in predicting the physicochemical properties of priority polycyclic aromatic hydrocarbons (PAHs) is explored. These Tis exhibited a strong correlation with certain properties of priority PAHs. Certain statistical aspects of these indices are discussed, and the significant results have also been represented graphically.

The analysis of hormonal status and vascular and cell proliferation in endometrioid endometrial adenocarcinomas.

Endometrioid endometrial carcinomas (EECs) are the most common malignancies of the uterus. Hormonal dependence of EEC, in relation to biomolecular mechanisms involved in tumor progression, such as angiogenesis and cell proliferation, are aspects that can contribute to improving the prognosis of patients. We analyzed the immunoexpression of markers addressed to steroid hormone receptors [estrogen receptor (ER), progesterone receptor (PR)], angiogenesis [cluster of differentiation (CD)105∕endoglin] and cell proliferation (Ki-67) in 50 EECs related to the histopathological prognostic criteria of the lesions. In this study, the ER and PR scores were higher in low grade and early stages EEC, the statistical aspects being variable. The CD105 microvessel density and the Ki-67 proliferation index were superior in high grade and advanced stages EEC, the statistical aspects being significant or at the limit of significance. The ER∕PR and CD105∕Ki-67 immunomarker groups indicated a positive linear intragroup relation and a negative linear intergroup relation, suggesting the presence of synergistic and antagonistic molecular mechanisms of tumor endometrial control that can be used to stratify patients for targeted therapy.

Statistical Issues in Serial Killer Nurse Cases

We study statistical aspects of the case of the British nurse Ben Geen, convicted of 2 counts of murder and 15 of grievous bodily harm following events at Horton General Hospital (in the town of Banbury, Oxfordshire, UK) during December 2013–February 2014. We draw attention to parallels with the cases of nurses Lucia de Berk (the Netherlands) and Daniela Poggiali (Italy), in both of which an initial conviction for multiple murders of patients was overturned after the reopening of the case. We pay most attention to the investigative processes by which data, and not just statistical data, is generated; namely, the identification of past cases in which the nurse under suspicion might have been involved. We argue that the investigation and prosecution of such cases are vulnerable to many cognitive biases and errors of reasoning about uncertainty, exacerbated by the fact that fact-finders have to determine not only whether a particular person was guilty of certain crimes, but whether any crimes were committed by anybody at all. The paper includes some new statistical findings on the Ben Geen case and suggests further avenues for investigation. The experiences recounted here have contributed to the writing of the handbook Healthcare Serial Killer or Coincidence? Statistical Issues in Investigation of Suspected Medical Misconduct, Royal Statistical Society, London, 2022.

Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread

The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships.

Towards Integrated Design and Operation of Complex Engineering Systems With Predictive Modeling: State-of-the-Art and Challenges

Abstract Growing trends towards increased complexity and prolonged useful lives of engineering systems present challenges for system designers in accounting for the impacts of post-design activities (e.g., manufacturing, condition monitoring, remaining life prediction, maintenance, service logistics, end-of-life options, etc.) on system performance (e.g., costs, reliability, customer satisfaction, environmental impacts, etc.). It is very difficult to develop accredited lifecycle system performance models because these activities only occur after the system is built and operated. Thus, system design and post-design decision-making have traditionally been addressed separately, leading to suboptimal performance over the systems lifecycle. With significant advances in computational modeling, simulation, sensing & condition monitoring, and machine learning & artificial intelligence, the capability of predictive modeling has grown prominently over the past decade, leading to demonstrated benefits such as improved system availability and reduced operation and maintenance costs. Predictive modeling can bridge system design and post-design stages and provide an optimal pathway for system designers to effectively account for future system operations at the design stage. In order to achieve optimal performance over the system’s lifecycle, post-design decisions and system operating performance can be incorporated into the initial design with the aid of state-of-the-art predictive modeling approaches. Therefore, optimized design and operation decisions can be explored jointly in an enlarged system design space. This article conducted a literature review for the integrated design and operation of engineering systems with predictive modeling, where not only the predictive modeling approaches but also the strategies of integrating predictive models into the system design processes are categorized. Although predictive modeling has been handled from data-driven, statistical, analytical, and empirical aspects, and recent design problems have started to evaluate the lifecycle performance, there are still challenges in the field that require active investigation and exploration. So, in the end, this article provides a summary of the future directions that encourages research collaborations among the various communities interested in the optimal system lifecycle design.

Some Statistical Aspects of the Truncated Multivariate Skew-t Distribution

The multivariate skew-t distribution plays an important role in statistics since it combines skewness with heavy tails, a very common feature in real-world data. A generalization of this distribution is the truncated multivariate skew-t distribution which contains the truncated multivariate t distribution and the truncated multivariate skew-normal distribution as special cases. In this article, we study several distributional properties of the truncated multivariate skew-t distribution involving affine transformations, marginalization, and conditioning. The generation of random samples from this distribution is described.

COMPARATIVE STUDY OF WHITE GAUSSIAN NOISE REDUCTION FOR DIFFERENT SIGNALS USING WAVELET

The present work is an attempt to make a comparative study of the wavelet-based noise reduction algorithm. The algorithm has been developed and implemented in MATLAB GUI and then analyzed for different types of wavelets such as Coiflet, Daubechies, Symlet, and Biorthogonal with their different versions. This algorithm has been verified for different types of input signals from different domains. Various statistical aspects like Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) are analyzed for this algorithm. It is observed that the developed algorithm for noise reduction using wavelet works very well for different types of wavelets.

Statistical Aspects of the Quantum Supremacy Demonstration

In quantum computing, a demonstration of quantum supremacy (or quantum advantage) consists of presenting a task, possibly of no practical value, whose computation is feasible on a quantum device, but cannot be performed by classical computers in any feasible amount of time. The notable claim of quantum supremacy presented by Google’s team in 2019 consists of demonstrating the ability of a quantum circuit to generate, albeit with considerable noise, bitstrings from a distribution that is considered hard to simulate on classical computers. Very recently, in 2020, a quantum supremacy claim was presented by a group from the University of Science and Technology of China, using a different technology and generating a different distribution, but sharing some statistical principles with Google’s demonstration. Verifying that the generated data is indeed from the claimed distribution and assessing the circuit’s noise level and its fidelity is a statistical undertaking. The objective of this paper is to explain the relations between quantum computing and some of the statistical aspects involved in demonstrating quantum supremacy in terms that are accessible to statisticians, computer scientists, and mathematicians. Starting with the statistical modeling and analysis in Google’s demonstration, which we explain, we study various estimators of the fidelity, and different approaches to testing the distributions generated by the quantum computer. We propose different noise models, and discuss their implications. A preliminary study of the Google data, focusing mostly on circuits of 12 and 14 qubits is given in different parts of the paper.

Transformed Semicircular Exponentiated Weibull Distribution

In this paper, a new transformed Semicircular Exponentiated Weibull ( SEW ) distribution of three parameters is constructed by applying the transformation of the inverse stereographic projection that maps a real line to the unit circle. The cumulative distribution function, probability density function, expanded probability density function, and reliability measures are presented. Furthermore, the most important statistical aspects of this new distribution, such as moments, trigonometric moments, characteristic function, simulated data, quantile function, reliability stress strength model, Shannon entropy, and relative entropy are obtained. The maximum likelihood estimation method is implemented to estimate the unknown three parameters. A simulation study is conducted to detect empirically the behaviors of the maximum likelihood estimates of the SEW parameters for different default values of the parameters and different samples size.

Visualization and quantitative statistics of experimental hydraulic fracture network based on optical scanning

The formation of hydraulic fracture-network is an important goal for stable and increased production of unconventional oil and gas resources. However, due to the extremely complex mechanical behavior of hydraulic-fracture propagation, there is still a lack of fine, effective and systematic evaluation methods on 3D fracture-network structure. The quantitative characterization of hydraulic-fractures is of great significance to reveal the law of fracture propagation and to evaluate the reservoir stimulation effectiveness reasonably. Although the micro-cracks evolution process follows a random rather than deterministic pattern, the overall fracture morphology is featuring statistical regularity. In this study, after the indoor hydraulic-fracturing simulation experiments of the tight sandstone specimens, 3D optical scanning was used to visualize the fracture-network. On this basis, two aspects of quantitative statistics of 3D complex fracture-networks were established by plane and sphere cuttings for the first time, including angle distribution statistics and spatial variation statistics. Comparative analyses with reported methods showed that, the overall deflection of the macroscopic fracture-networks varies synchronously with the local roughness of the main hydraulic fractures closest to the wellbore, which satisfied exponential function or power-law function relationship between them. The variation of the area proportion with the propagation radius reflects the specific fracture propagation process such as crack initiation, bifurcation and arrest, and could be used to assess the complexity of the fracture-network. Moreover, a new stimulation evaluation index Es was established by comprehensively considering the stimulated-reservoir-area, the dispersion degree of the angle distribution and the complexity of spatial variation for 3D fracture-network. The insights gained warrant further applicability on hydraulic-fracturing in the actual engineering scale.

Bayesian unanchored additive models for component network meta-analysis.

Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a more restrictive form of additivity than the other which we term an anchored and unanchored model, respectively. We show that an anchored model can provide a poor fit to the data if it is misspecified. Second, given that Bayesian models are often preferred by practitioners, we develop two novel unanchored Bayesian CNMA models presented under the unified notation. An extensive simulation study examining bias, coverage probabilities, and treatment rankings confirms the favorable performance of the novel models. This is the first simulation study to compare the statistical properties of CNMA models in the literature. Finally, the use of our novel models is demonstrated on a real dataset, and the results of CNMA models on the dataset are compared.

Generation of the Forest Cover Map of the Qinghai–Tibet Plateau Based on the Multisource Dataset and Random Forest

The Qinghai–Tibet Plateau (QTP) plays a significant role in global climate change and biodiversity conservation. As the third pole of the Earth, it has a wide range and complex terrain. QTP has a vertical distribution of vegetation, and its forest ecosystems play a key role in the region. Forest extraction in this region is still a comprehensive problem because of the phenological periods of different forest types in distinct regions of the QTP and the characteristics of frequent rain and cloudy weather in the south. Taking these factors into consideration, multiple features, including reflectance, spectral indices, statistical backscattering coefficients, topographic slope, and aspect, derived from a multisource dataset incorporating optical remote sensing data, synthetic radar, and digital elevation models, were applied to extract forest in the QTP based on the random forest (RF) classification method. As more than 30 features were involved, the 5-folded cross-validation method was used to determine the optimal parameters and features for the RF model. Using 14,919 forest samples and a multifeature optimized RF classification model, a 10-m resolution forest cover map of QTP in 2021 was generated based on the Sentinel series of satellite datasets and digital elevation model datasets on the Google Earth Engine (GEE) platform. After verification, the overall accuracy of the forest cover map generated in this article is 98.3%, and the Kappa coefficient is 0.95, which is better than the European Space Agency (ESA) WorldCover forest layer.

State Aid for Development of Manufacture of Agricultural Machinery: Economic and Statistical Aspect

The world practice of state support of agricultural machinery is studied. Features of dualistic agro-industrial policy of China are identified. It is substantiated that the subsidy policy for the purchase of agricultural machinery, which has been implemented by the Chinese government since 2004, has also had an extensive impact on the promotion and development of agricultural mechanization, development of agricultural machinery and improvement of economic results, scaling up of services for agricultural sector; activation of related industries: the development of companies that specialize in construction and road equipment, the production of engineering products for agricultural sector. The evolution of state support is shown: changes in the priorities of industry development and the number of subsidies for the purchase of equipment, the development of which is encouraged. It is established that the support of decision-making on the implementation of state support is provided by scientific institutions, relevant associations, information and analytical agencies. It is shown that the generated statistical databases allow to analyze the scale and structure of sales of machinery to farmers with state subsidies (by categories, manufacturers, regions), calculate China's agricultural machinery market sentiment index (AMI), among the components of which - Agricultural Machinery Subsidy Index.
 It is stated that on the initiative of the National Committee for Industrial Development a mechanism of partial compensation of the cost of machinery and equipment for agro-industrial complex, which has a certain level of localization of production in Ukraine, was launched in Ukraine in 2017. Summing up the results of the implementation of this mechanism for the national economy are presented. The necessity of expanding state support is substantiated: introduction of incentives for technological innovations of enterprises and partial compensation of interest on loans for the purchase of Ukrainian made agricultural machinery. Following the example of China, it is proposed to improve the mechanism of partial compensation by adjusting the upper limit of aid intensity in accordance with the cost of machinery and equipment for farmers and its innovation. It is substantiated that the lack of multifaceted monitoring of the implementation of the partial compensation mechanism makes it impossible to assess its consequences for machine builders, farmers and the economy as a whole (primarily in terms of the effects obtained). The introduction of statistical and information-analytical support for the implementation of the state aid mechanism in Ukraine will facilitate both timely control and management decisions-making on future support by the state, and the strategic business planning of farmers and machine builders.

On the Kumaraswamy Pseudo-Lindley distribution : statistical properties, extremal characterization and record values

The Pseudo-Lindley distribution is generalized in this paper by using the Kumaraswamy-G distribution developed by Cordeiro et al. (2010a). By fusing the Pseudo-Lindley distribution in the Kumaraswamy generator of distribution, we present a new unique four parameters of continuous model of distribution titled the Kumaraswamy Pseudo-Lindley distribution (<i>Kum-PL</i>). The moments and related measures, moment generating function , order statistic, uniform quantile and extremal quantile function are all carefully examined as basic statistical aspects of the new distribution. Moreover, the extremal characterizations and record values of the new model are investigated. The proposed distribution parameters were evaluated using the maximum likelihood procedures. The stability of the parameter estimations is explored using a Monte Carlo simulation study

АНАЛИЗ ДЕЯТЕЛЬНОСТИ КОМИССИИ ПО ВОПРОСАМ КУЛЬТОВ ПРИ ПРЕЗИДИУМЕ ВЦИК В 1929–1930 ГГ.

The article deals with the work of the Commission on Cults under the Presidium of the All-Russian Central Executive Committee in 1929–1930. The research topic is relevant, because. Currently, there is a rethinking of the Soviet experience of interaction between state power and the Russian Orthodox Church in the Soviet period of our history. The purpose of the study is to analyze the work of the Commission on Cult Issues and to understand its role in the development of the anti-religious policy of the Soviet government. This article examines the problems faced by the commission in resolving issues related to the interaction of the authorities, the population and the church. A great contribution to the study of the activities of the Commission on Cults was made by O.B. Prikazchikova, A.S. Kochetova, A.L. Ershov. The merit of these authors lies in the fact that they analyzed the statistical aspect of the commission's work. The main emphasis in their articles is placed on the period from 1931 to 1935. This article examines in detail the period of work of the commission from 1929 to 1930. A new substantive aspect of the commission's work is considered. The methodology of this article includes the historical-diachronic method, which made it possible to understand the internal laws of the development of the political course of the Soviet government in relation to the Russian Orthodox Church and clergy. The comparative historical method made it possible to compare some aspects of the policy of the state power of the Russian Empire and the Soviet government in relation to the Orthodox Church. The structural-system method made it possible to study the genesis of the policy of the Soviet government on the issue of its interaction with the Russian Orthodox Church in its entirety and in certain aspects. As a result of the study, it was possible to study the complaints and petitions received by the commission, the reaction of the commission members to these citizens' appeals. The activity of the chairman of the commission of the Soviet state and party leader P.G. Smidovich. In this study, archival materials from the collections of the State Archives of the Russian Federation (GA RF) are introduced into scientific circulation for the first time. The materials and conclusions contained in the study may be useful for teaching the "History of Russia", in the preparation of textbooks, special courses on the history of interaction between the Soviet government and the Russian Orthodox Church. The Commission made decisions regarding the Russian Orthodox Church and clergy in accordance with the current legislation of the USSR at that time. However, this legislation itself was aimed at excluding the church from social and political life and the complete eradication of faith from the minds of citizens.

Noise and bias – some controversies raised by the book ‘Noise: A Flaw in Human Judgment’, written by Daniel Kahneman, Olivier Sibony, Cass R. Sunstein

The paper reviews and discusses the statistical aspects of the phenomenon called ‘noise’ which Daniel Kahneman, the Nobel Prize winning psychologist, and his colleagues present in their new book entitled ‘Noise: A Flaw in Human Judgment’. Noise is understood by the authors as an unexpected and undesirable variation present in people’s judgments. The variability of judgments influences decisions which are made on the basis of those judgments and, consequently, may have a negative impact on the operations of various institutions. This is the main concern presented and analyzed in this book. The objective of this paper is to look at the relationship between bias and noise – the two major components of the mean squared error (MSE) – from a different perspective which is absent in the book. Although the author agrees that each of the two components contributes equally to MSE, he claims that in some circumstances a reduction of noise can make accurate inference not less, but more difficult. It is justified that the actual impact of noise cannot be accurately determined without considering both bias and noise simultaneously.

Effects of Anonymity versus Examinee Name on a Measure of Depressive Symptoms in Adolescents.

There is evidence in the literature that anonymity when investigating individual variables could increase the objectivity of the measurement of some psychosocial constructs. However, there is a significant gap in the literature on the theoretical and methodological usefulness of simultaneously assessing the same measurement instrument across two groups, with one group remaining anonymous and a second group revealing identities using names. Therefore, the aim of this study was to compare the psychometric characteristics of a measure of depressive symptoms in two groups of adolescents as a consequence of identification or anonymity at the time of answering the measuring instrument. The participants were 189 adolescents from Metropolitan Lima; classrooms were randomly assigned to the identified group (n = 89; application requesting to write one’s own name) or to the anonymous group (n = 100; application under usual conditions), who responded to the Childhood Depression Inventory, short version (CDI-S). Univariate characteristics (mean, dispersion, distribution), dimensionality, reliability, and measurement invariance were analyzed. Specific results in each of the statistical and psychometric aspects evaluated indicated strong psychometric similarity. The practical and ethical implications of the present results for professional and research activity are discussed.

Multi-population-based adaptive sine cosine algorithm with modified mutualism strategy for global optimization

The sine cosine algorithm (SCA) is a population-based metaheuristic strategy that has been demonstrated competitive performance and has received significant attention from scientists in various fields. Regardless, like other population-based techniques, SCA also has a tendency to get stuck in adjacent optima and uneven exploitation. Given the shortcomings of SCA, a new modified SCA variation, MAMSCA, with a balanced global and local search, is presented in this work. The new method divides the population into two equal halves for updating using a sine or cosine strategy. To provide additional variation to the population, a modified mutualism phase is adopted. To increase convergence speed and accuracy of the solution, a one-to-one mapping between the individuals of each half is maintained. The new algorithm is compared with state-of-the-art algorithms and modified algorithms using classical benchmark functions and IEEE CEC 2019 functions. The method’s real-world relevance is demonstrated by tackling five engineering design challenges. The comparison of numerical results and analysis of performance measures of the algorithm from the statistical aspect, time complexity, and speed of generating the solution demonstrates the considerable improvement of the proposed algorithm to solve real-world challenges.

TECHNICAL AND ECONOMIC ASSESSMENT OF MARITIME ACCIDENTS

This article highlights the main technical and statistical aspects related to maritime accidents, including the various types of vessels that navigate rivers and oceans around the world. It seeks to understand the mechanics of different accidents, their recurrence for different causes and their financial impact. Technically, through case studies, it shows the different types of accidents that occur in different global locations. The preservation of human life as well as of the environment are determining factors in any maritime accident today.
 
 Keywords: Maritime Casualties, Class Society, International Maritime Organization, Environment, Costs, Cases

Design and Analysis of Studies Based on Hierarchical Composite Endpoints: Insights from the DARE-19 Trial.

Background/Aim DARE-19 (NCT04350593) was a randomized trial studying the effects of dapagliflozin, an SGLT2 inhibitor, in hospitalized patients with COVID-19 pneumonia and cardiometabolic risk factors. The conduct of DARE-19 offered the opportunity to define an innovative and clinically meaningful endpoint in a new disease that would best reflect the known profile of dapagliflozin, accompanied by the statistical challenges of analysis and interpretation of such a novel endpoint.MethodsHierarchical composite endpoints (HCEs) are based on clinical outcomes which, unlike traditional composite endpoints incorporate ranking of components according to clinical importance. Design of an HCE requires the clinical considerations specific to the therapeutic area under study and the mechanism of action of the investigational treatment. Statistical aspects for the clinical endpoints include the proper definition of the estimand as suggested by ICH E9(R1) for the precise specification of the treatment effect measured by an HCE.ResultsWe describe the estimand of the DARE-19 trial, where an HCE was constructed to capture the treatment effect of dapagliflozin in hospitalized patients with COVID-19, and was analyzed using a win odds. Practical aspects of designing new studies based on an HCE are described. These include sample size, power, and minimal detectable effect calculations for an HCE based on the win odds analysis, as well as handling of missing data and the clinical interpretability of the win odds in relation to the estimand.ConclusionsHCEs are flexible endpoints that can be adapted for use in different therapeutic areas, with win odds as the analysis method. DARE-19 is an example of a COVID-19 trial with an HCE as one of the primary endpoints for estimating a clinically interpretable treatment effect in the COVID-19 setting.