Statistically Research Articles

The association between monocyte-to-high-density lipoprotein ratio and hyperuricemia: Results from 2009 to 2018.

Previous research has suggested that the monocyte-to-high-density lipoprotein ratio (MHR), an emerging inflammatory biomarker, holds promise in predicting the prevalence of various cardiovascular and metabolic diseases. However, earlier investigations were constrained by the relatively modest sample sizes. This study endeavored to expand the sample size and conduct a more comprehensive exploration of the potential relationship between MHR and hyperuricemia. This cross-sectional study incorporated data from participants of the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) with complete and qualifying information. MHR was determined by calculating the ratio between monocyte count and high-density lipoprotein levels. Various statistical methodologies such as weighted multivariate logistic regression, subgroup analysis, smoothed curve fitting, and threshold analysis, have been used to explore the correlation between hyperuricemia and MHR. The study included a cohort of 17,694 participants, of whom 3512 were diagnosed with hyperuricemia. MHR levels were notably higher in the hyperuricemia group than in the normal group, aligning with an elevated body mass index (BMI). A comprehensive multivariate logistic analysis, accounting for all relevant adjustments, revealed a notable positive correlation between MHR and hyperuricemia (P < .001, OR = 1.98, 95% CI: 1.54-2.54). Subgroup analysis indicated that the MHR exhibited an enhanced predictive capacity for identifying hyperuricemia risk, particularly in females (P < .05). Curvilinear and threshold analyses revealed a nonlinear association between MHR and hyperuricemia prevalence, with a notable inflection point at 0.826. In the US population, a clear positive correlation was observed between the MHR and prevalence of hyperuricemia. Importantly, the MHR is a more robust predictor of hyperuricemia risk in females. Further investigations are required to confirm these findings.

The influence of teachers’ statistics knowledge on the academic performance of high school students in math lessons

Statistics and probability is one branch of mathematics with numerous conceivable outcomes and problem-solving circumstances that require a basic problem-solving aptitude. The purpose of this study is to determine the need for high school mathematics teachers to teach statistics and probability, which would serve as a basis for improving the level of teaching statistics and mathematics in general. A descriptive research method was applied in this study, and a two-part survey was employed as the primary method of data collection. A total of 34 secondary school mathematics teachers from 23 private and public secondary schools in Kazakhstan acted as respondents to answer a specially designed questionnaire. The mean value, percentage of frequency, standard deviations, correlation, regression, percentage of correct answers and average percentage scores were used. Based on the responses, the respondents have a low level of knowledge regarding the content of statistics, but their students have satisfactory results in statistics and mathematics. The results of this study will give scientists an idea of how to improve the level of training of mathematics teachers. In addition, since higher educational institutions are engaged in the training of mathematics teachers, they need to think about the methodology of teaching statistics, as well as teacher training institutes, to adapt courses and seminars for teachers.

A mini-review on perturbation modelling across single-cell omic modalities

Recent advances in single-cell omics technology have transformed the landscape of cellular and molecular research, enriching the scope and intricacy of cellular characterisation. Perturbation modelling seeks to comprehensively grasp the effects of external influences like disease onset or molecular knock-outs or external stimulants on cellular physiology, specifically on transcription factors, signal transducers, biological pathways, and dynamic cell states. Machine and deep learning tools transform complex perturbational phenomena in algorithmically tractable tasks to formulate predictions based on various types of single-cell datasets. However, the recent surge in tools and datasets makes it challenging for experimental biologists and computational scientists to keep track of the recent advances in this rapidly expanding filed of single-cell modelling. Here, we recapitulate the main objectives of perturbation modelling and summarise novel single-cell perturbation technologies based on genetic manipulation like CRISPR or compounds, spanning across omic modalities. We then concisely review a burgeoning group of computational methods extending from classical statistical inference methodologies to various machine and deep learning architectures like shallow models or autoencoders, to biologically informed approaches based on gene regulatory networks, and to combinatorial efforts reminiscent of ensemble learning. We also discuss the rising trend of large foundational models in single-cell perturbation modelling inspired by large language models. Lastly, we critically assess the challenges that underline single-cell perturbation modelling while pointing towards relevant future perspectives like perturbation atlases, multi-omics and spatial datasets, causal machine learning for interpretability, multi-task learning for performance and explainability as well as prospects for solving interoperability and benchmarking pitfalls.

The application of statistical and novel unsupervised machine learning methodology to forensic hair analysis

ABSTRACT Hair colour is a valuable feature in forensic hair analysis and hair comparisons. Five hundred microscopic images of hair shafts were taken and for each image and colour model (RGB, CIE XYZ and CIE L*a*b*) values for each image were determined. The discriminating power of the three colour model values using statistical and unsupervised machine learning methods was evaluated. Additionally, the discriminating power of the images of the same hair shafts using unsupervised machine learning methods was evaluated. All methods were compared to determine which method had the greatest discriminating power to best distinguished between participants and accurately assigned hair to an individual and assist in forensic hair comparisons. The RGB colour model demonstrated the highest discriminating power of the colour model values while the CIE L*a*b* model achieved complete discrimination with a reduced number of participants. The unsupervised k-means model yielded similar results. Unsupervised PCA/k-means and agglomerative clustering models demonstrated low discrimination power of the images, suggesting the existence of additional features within the data beyond colour. This research highlights the significance of the incorporation of colour values in forensic hair comparisons and for further exploration of the incorporation of other hair features, beyond colour values.

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke.

We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations. We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions. A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit. Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

Mendelian randomization: causal inference leveraging genetic data.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

A Multi-Method Approach to the Future of the Scholarship of Teaching and Learning

Since the establishment of the Journal of Political Science Education (JPSE) in 2004, the Scholarship of Teaching and Learning has become more rigorous and data-based, thanks in large part to the efforts of editors, peer-reviewers, and submitters to the journal. Where does the field go from here? I argue that our greatest strengths lie in multimethod approaches. To best understand how to improve teaching and learning in political science, we need to use all of our methodological tools. To illustrate these tools, I provide examples of some of the most-cited pieces in JPSE over the past 20 years, including ones using large-n statistical, experimental, case study, student self-report, qualitative evaluation, and reflective methodologies. I also provide examples of multimethod SoTL work and argue that the best way to move the field forward is to use multiple methodological tools together. Multimethod approaches help us better understand both the teaching problems that are facing the field of political science today and how best to respond to them. For instance, big data can help us understand overall trends, but may hide the individual experiences of students; experiments may reveal causal relationships, but surveys can help us understand how they function in practice. By bringing multiple methodological approaches together, we can get better answers to important questions. The widespread adoption of multimethod SoTL approaches in the future will require rethinking graduate school training, thoughtful research design, and providing intentional collaboration opportunities through conferences and infrastructure.

Post-kala-azar dermal leishmaniasis (PKDL) drug efficacy study landscape: A systematic scoping review of clinical trials and observational studies to assess the feasibility of establishing an individual participant-level data (IPD) platform.

Post-kala-azar dermal leishmaniasis (PKDL) is a dermatosis which can occur after successful treatment of visceral leishmaniasis (VL) and is a public health problem in VL endemic areas. We conducted a systematic scoping review to assess the characteristics of published PKDL clinical studies, understand the scope of research and explore the feasibility and value of developing a PKDL individual patient data (IPD) platform. A systematic review of published literature was conducted to identify PKDL clinical studies by searching the following databases: PubMed, Scopus, Ovid Embase, Web of Science Core Collection, WHO Global Index Medicus, PASCAL, Clinicaltrials.gov, Ovid Global Health, Cochrane Database and CENTRAL, and the WHO International Clinical Trials Registry Platform. Only prospective studies in humans with PKDL diagnosis, treatment, and follow-up measurements between January 1973 and March 2023 were included. Extracted data includes variables on patient characteristics, treatment regimens, diagnostic methods, geographical locations, efficacy endpoints, adverse events and statistical methodology. A total of 3,418 records were screened, of which 56 unique studies (n = 2,486 patients) were included in this review. Out of the 56 studies, 36 (64.3%) were from India (1983-2022), 12 (21.4%) from Sudan (1992-2021), 6 (10.7%) were from Bangladesh (1991-2019), and 2 (3.6%) from Nepal (2001-2007). Five (8.9%) studies were published between 1981-1990 (n = 193 patients), 10 (17.9%) between 1991-2000 (n = 230 patients), 10 (17.9%) between 2001-2010 (n = 198 patients), and 31 (55.4%) from 2011 onwards (n = 1,865 patients). Eight (14.3%) were randomised clinical trials, and 48 (85.7%) were non-randomised studies. The median post-treatment follow-up duration was 365 days (range: 90-540 days) in 8 RCTs and 360 days (range: 28-2,373 days) in 48 non-randomised studies. Disease diagnosis was based on clinical criterion in 3 (5.4%) studies, a mixture of clinical and parasitological methods in 47 (83.9%) and was unclear in 6 (10.7%) studies. Major drugs used for treatment were miltefosine (n = 636 patients), liposomal amphotericin B (L-AmB) (n = 508 patients), and antinomy regimens (n = 454 patients). Ten other drug regimens were tested in 270 patients with less than 60 patients per regimen. Our review identified studies with very limited sample size for the three major drugs (miltefosine, L-AmB, and pentavalent antimony), while the number of patients combined across studies suggest that the IPD platform would be valuable. With the support of relevant stakeholders, the global PKDL community and sufficient financing, a PKDL IPD platform can be realised. This will allow for exploration of different aspects of treatment safety and efficacy, which can potentially guide future healthcare decisions and clinical practices.

Utilization Trends of Phosphodiesterase Type-5 Inhibitors for Erectile Dysfunction Between 2019 and 2023 in Tanzania.

Introduction Erectile dysfunction (ED) profoundly affects millions of people globally, including interfering with mental health and quality of life. Phosphodiesterase type-5 inhibitors (PDE5Is) such as sildenafil are pivotal in ED treatment. This study aimed to examine the utilization patterns ofPDE5Is in Tanzania. Materials and methods In this retrospective longitudinal study, data on sildenafil and other similar PDE5Is imported between 2019 and 2023 were sourced from the Tanzania Medicines and Medical Devices Authority (TMDA). Pre-processing and visualization were performed using Microsoft Power BI Desktop, and further analysis was performed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York, United States). Utilization trends were ascertained through curve fitting, Holt's linear trend model, and autoregressive integrated moving average (ARIMA) models. The defined daily doses (DDDS) per 1000 inhabitants (DID) were calculated using the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) Classification System and the DDD methodology endorsed by the WHO Collaborating Centre for Drug Statistics Methodology. Results Between 2019 and 2023, there was a pronounced increase in the importation of approximately 587 consignments of PDE5Is. Employing the Holt model (R-square = 0.843), a substantial increase from 0.220910 DID in 2019 to 0.534272 DID by 2025 was observed and anticipated. The period witnessed sildenafil dominating 75.5% of the total use, with Erecto being the most consumed brand (37.6% of totalDID). Notably, 2022 had the highest surge (27.2% of the total), albeit a slight decline was observed in 2023 (20.5%). This trend was supported by a linear regression model (R-square = 0.889). Conclusion We found increasing annual trends of PDE5Is of utilization. This requires critical oversight and effective policies to ensure appropriate use and risk minimization.

Statistical mirroring: A robust method for statistical dispersion estimation

This study introduces statistical mirroring as an innovative approach to statistical dispersion estimation, drawing inspiration from the Kabirian-based isomorphic optinalysis model, aimed at enhancing robustness and mitigating biases in estimation methods. Beyond scale-invariant characteristics, the proposed estimators emphasize scaloc-invariant robustness, thereby addressing a critical gap in dispersion estimation. By highlighting statistical meanic mirroring, alongside other forms of proposed statistical mirroring, the study underscores the adaptability and customization potential. Through extensive Monte Carlo simulations and real-life applications, in comparison with classical estimators, the results of the performance evaluation of the proposed estimators demonstrate robustness, efficiency, and transformations-invariance. The research offers a paradigm shift in addressing longstanding challenges in dispersion estimation, offering a new category of dispersion estimation and increased resistance to outliers. Notable limitations include selecting and evaluating the proposed statistical meanic mirroring under Gaussian and Gaussian mixture model distributions. This research paper significantly contributes to statistical methodologies, offering avenues for expanding knowledge in dispersion estimation. It recommends further exploration of proposed estimators across various statistical mirroring types and encourages comparative studies to establish their effectiveness, thereby advancing statistical knowledge and tools for precise data analysis.•The proposed methodology involves preprocessing transformations, statistical mirror design, and optimization to transform a univariate set into a bivariate one, facilitating the fitting of an isomorphic optinalysis model.•Estimators rely on a foundational bijective mapping of isoreflective pairs, deducing the probability of proximity or deviation from any defined center. This contrasts with classical estimators that utilize average or median deviations from a mean or median center.

Small area estimation of labour force indicators under unit-level multinomial mixed models

Abstract This paper presents a new statistical methodology for the small area estimation of the proportion of employed, unemployed and inactive people, and of unemployment rates. The novel empirical best and plug-in predictors are based on a multinomial mixed model that is fitted to unit-level data. Model parameters are estimated by maximum-likelihood and mean-squared errors by parametric bootstrap. Several simulation experiments are carried out to empirically investigate the properties of these estimators and predictors. Finally, a detailed application to real data from the first Spanish Labour Force Survey of 2021 is included, where the target is to map labour force indicators by province, sex, and age group.

Trends of Acute Myocardial Infarction Mortality in People Over 65 Years Old in the United States From 1999-2020: Insight From the CDC-WONDER Database.

Background Over the past two decades, there have been numerous advances in acute myocardial infarction (AMI) care. We assessed the impact of these advances on the trend of AMI-related mortality. Methods This retrospective analysis of the Centers for Disease Control's Wide-ranging Online Data for Epidemiologic Research (CDC_WONDER) database focused on AMI-related mortality in individuals aged 65 and older in the United States from 1999 to 2020. Trends -n crude and age-adjusted mortality rates (AAMR) were assessed based on socio-demographic and regional variables using Joinpoint Regression software (Joinpoint Regression Program, Version 5.0.2 - May 2023; Statistical Methodology and Applications Branch, Surveillance Research Program,National Cancer Institute Bethesda, Maryland). Annual percentage change (APC) with 95% confidence intervals (CIs) for the AAMRs were calculated for the line segments linking a Joinpoint using a data-driven weighted Bayesian Information Criterion (BIC) model. Results There were 2,354,971 AMI-related deaths with an overall decline in the AAMR from 474.6 in 1999 to 153.2 in 2020 and an average annual percentage change (AAPC) of -5.3 (95% CI -5.4 to -5.2). Notable declines were observed across gender, race, age groups, and urbanization levels. However, the rate of AMI-related deaths at decedents' homes slowed down between 2008 and 2020 and climbed up between 2018 and 2020. In addition to this, nonmetropolitan areas were found to have a significantly lower decline in mortality when compared to large and medium/small metropolitan areas. Conclusion While there is an overall positive trend in reducing AMI-associated mortality, disparities persist, emphasizing the need for targeted interventions.

Implementation of R Software in Improving Student Statistical Reasoning, is it Effectivities?

In the current era of digitalization, statistical skills are needed to interpret and understand and make good decisions about statistical data. However, many students still have the view that statistics is a difficult subject to understand. This is based on the fact that in working on the problem sufficient statistical skills are needed, starting from basic skills to application abilities. Students who have low statistical abilities become less interested in statistics lectures. One of these statistical abilities is statistical reasoning. Statistical reasoning is the student's ability to make logical conclusions from a problem by using statistical concepts in a comprehensive manner, and making decisions as well as evaluating and interpreting the information processed. Efforts to improve statistical reasoning skills require a learning innovation using an application. The role of technology in learning statistics includes using modules assisted by the R program. The research method used is quasi-experimental with a research design, namely pretest-posttest control group design. The results showed that the statistical reasoning abilities of students whose lectures used the R program assisted module were higher than regular lectures for both the low, medium and overall groups. However, the high group had the same increase in statistical reasoning abilities between the experimental and control classes. The effect of statistics lectures using the R program assisted module was high in the medium, low and overall groups. Thus, the use of the R program assisted module is effective in increasing statistical reasoning.

STATE REGULATION OF THE ICT SECTOR IN UKRAINE IN THE CONTEXT OF TECHNOLOGICAL TRANSFORMATION OF THE ECONOMY

The paper deals with the understanding of government strategies for regulating the ICT industry. The purpose of the study is to rationalise the directions of government regulation of the ICT industry in Ukraine in the context of technological transformation of the economy. Using the methodology of descriptive statistics, the authors revealed the rapid growth of the number of employees in the ICT industry in Ukraine. The authors conclude that, from the point of view of state regulation, given the fact that employment has proved to be the main factor in the success of exports, it is necessary to prevent the outflow of labour from Ukraine. This is confirmed by the competitive advantages gained by Ukrainian ICT workers in the global market, namely: price – the cost of Ukrainian coders' services is close to the prices of Indian coders and seven times less than the cost of coders' services in the US; diversity and high quality of technological competencies that allow flexible implementation of high-level ICT projects; its own research and development base, as well as materials and technologies for the implementation of complex knowledge-intensive projects, flexibility in the use and distribution of necessary resources; cultural and geographical proximity to Europe. Based on this, strategies for interaction between the state and the ICT sector are being developed. First, the state needs to create favourable conditions for the sustainable development of the industry, including reforming legal norms, improving investment policy, modernising labour legislation, strengthening the protection of intellectual property rights, and ensuring the return of labour from abroad. Second, to create comfortable conditions to reduce the outflow of intellectual resources abroad and strengthen Ukraine as a brand in the IT sector in the eyes of the international community. The main strategies for the formation of mechanisms for state support of information services are as follows: the emergence of a modern IT infrastructure accessible to all, improving the quality of information services; a strategy for creating a regulatory framework for the effective use of the network economy; a strategy for creating state indicators of the development of the network economy; a strategy for creating information security in network systems.

Tuning user-defined pavements in CNOSSOS-EU towards reliable estimates of road noise exposure

Low-noise pavements (LNPs) are among the most frequently used measures to mitigate road traffic noise. CNOSSOS-EU model enables improved predictions of noise exposure by customizing surface coefficients. However, accurately estimating their benefits to citizens remains a challenge. Specific LNPs can be defined by the user, but their calibration remains difficult, with different approaches in literature. Using standard roadside measurements and the recent Urban Statistical Pass-By methodology, this study presents a procedure for tuning user-defined coefficients of pavements in a real-world scenario. Test cases results are assessed against reference scenarios to evaluate the population exposure differences and reduction of potential harmful effects. The procedure can be a valuable instrument in aiding action plans in compliance with the 2002/49/EC Directive. The work also highlights the need to assess the acoustical characteristics of LNPs to effectively evaluate health benefits, as well as the need for comprehensive studies defining the exact characteristics of LNPs.

Bacterial and fungal community structures in Hulun Lake are regulated by both stochastic processes and environmental factors.

Microorganisms are a crucial component of lake ecosystems and significant contributors to biogeochemical cycles. However, the understanding of how primary microorganism groups (e.g., bacteria and fungi) are distributed and constructed within different lake habitats is lacking. We investigated the bacterial and fungal communities of Hulun Lake using high-throughput sequencing techniques targeting 16S rRNA and Internal Transcribed Spacer 2 genes, including a range of ecological and statistical methodologies. Our findings reveal that environmental factors have high spatial and temporal variability. The composition and community structures vary significantly depending on differences in habitats. Variance partitioning analysis showed that environmental and geographical factors accounted for <20% of the community variation. Canonical correlation analysis showed that among the environmental factors, temperature, pH, and dissolved oxygen had strong control over microbial communities. However, the microbial communities (bacterial and fungal) were primarily controlled by the dispersal limitations of stochastic processes. This study offers fresh perspectives regarding the maintenance mechanism of bacterial and fungal biodiversity in lake ecosystems, especially regarding the responses of microbial communities under identical environmental stress.IMPORTANCELake ecosystems are an important part of the freshwater ecosystem. Lake microorganisms play an important role in material circulation and energy flow owing to their unique enzymatic and metabolic capacity. In this study, we observed that bacterial and fungal communities varied widely in the water and sediments of Hulun Lake. The primary factor affecting their formation was identified as dispersal limitation during stochastic processes. Environmental and geographical factors accounted for <20% of the variation in bacterial and fungal communities, with pH, temperature, and dissolved oxygen being important environmental factors. Our findings provide new insights into the responses of bacteria and fungi to the environment, shed light on the ecological processes of community building, and deepen our understanding of lake ecosystems. The results of this study provide a reference for lake management and conservation, particularly with respect to monitoring and understanding microbial communities in response to environmental changes.

Statistical considerations in model-based dose finding for binary responses under model uncertainty.

The statistical methodology for model-based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore several key design and analysis issues related to the hybrid testing-modeling approaches for binary responses. The issues include candidate model selection and specifications, optimal design and efficient sample size allocations, and, notably, the methods for dose-response testing and estimation. Specifically, we consider a class of generalized linear models suited for the candidate set and establish D-optimal designs for these models. Additionally, we propose using permutation-based tests for dose-response testing to avoid asymptotic normality assumptions typically required for contrast-based tests. We perform trial simulations to enhance our understanding of these issues.

On fitting and forecasting the log-returns of Bitcoin and Ethereum exchange rates via a new sine-based logistic model and robust regression methods

Among the different financial sectors, the modeling and forecasting of log-returns of cryptocurrency have received considerable attention. Numerous statistical models have been put forward to analyze the log returns of the cryptocurrency. However, as per our knowingness and immense literature search, we did not find published shreds of evidence about modeling cryptocurrency's log-returns while manipulating trigonometric-based statistical models. This paper provides a worthwhile endeavor to fill out this amusing research gap by manipulating a new trigonometric-based statistical methodology called the generalized sine-G family. Utilizing the generalized sine-G, a statistical model called the generalized sine-Logistic distribution is introduced. The generalized sine-Logistic distribution is applied for modeling the log-returns of two cryptocurrencies. Using certain decisive tools, it is observed that the generalized sine-Logistic is the best-suited distribution for modeling the given log-returns data sets. Additionally, this study uses various sophisticated and robust econometric techniques, such as the Least Absolute Shrinkage and Subset Selection, Markov Switching Generalized Autoregressive Conditional Heteroscedasticity (MSGARCH), and Step Indicator Saturation (SIS) model with different distributions, to predict (in-sample) the log-returns data sets. The effectiveness of each method is assessed through a popular loss function known as the root-mean-square error (RMSE).

Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

In metrology systems, machine vision systems are often utilized for non-contact inspection. The most important phase in ensuring measurement accuracy is camera calibration and estimation of pixel measurement errors, which establish the correspondence between image coordinates and object coordinates. Multiple calibration techniques improve the effectiveness of machine vision systems. However, a number of factors lead to variations in the camera calibration procedure, which must be optimized. This study explains a novel ‘Cyclic-Lead-Follower’ statistical methodology proposed for camera calibration and measurement to estimate the errors in pixel measurement, employing four slip gauges for measurement. Several multi-criteria decision-making techniques, including WSM, WPM, WASPAS, and TOPSIS, were used to optimize the results of the proposed Cyclic-Lead-Follower methods. The proposed Cyclic-Lead-Follower method improves the accuracy of the camera calibration and measurement system, which employs the exponential moving average statistical method when compared to the traditional calibration method. The proposed calibration method produces lower exponential moving average values than the traditional calibration method, with an average percentage error of approximately 46% in the exponential moving average. The use of an exponential moving average in a validation experiment of the Cyclic-Lead-Follower method decreased the measurement percentage errors, which were estimated to be 0.57%. The proposed method can be used in machine vision systems due to its robustness, accuracy, and cost-effectiveness.

A method for homogenization of complex daily mean temperature data: Application at Beijing Observatory (1915–2021) and trend analysis

AbstractA homogenized daily mean temperature series from 1915 to 2021 at Beijing Observatory (BO) has been developed. A thorough investigation of observation records and historical metadata was carried out to identify specific non‐climatic biases. The inhomogeneities were detected and adjusted with the optimally selected reference stations using a statistical reduction methodology. The results indicated three types of non‐climatic biases in temperature records: the first was the relocation of BO from suburban to urban, which caused positive shifts in temperature records, especially for wintertime; the second arose from different methods for calculating daily mean temperature, which caused different sign biases varying with months; and the last was caused by the transition from manual to automatic measurements with site‐specific biases. The corresponding adjustments due to three type biases ranged from −1.13 to 0.63, −0.29 to 0.23 and −0.13 to 0.00°C, respectively. The new homogenized annual mean temperature series showed a warming trend of 0.199°C/decade during 1915–2021. The trend was more pronounced in winter than in the other three seasons. The warming trend in the new series is greater than those in the previous homogenized series (0.136–0.177°C/decade), primarily due to the more effective adjustment associated with the site relocation, particularly for more urban locations.