Statistical Data Modeling Research Articles

Variations of flare energy release behaviour and magnetic loop characteristics versus absolute stellar parameters

ABSTRACT In this paper, we examine how stellar flare activity varies with evolution stage, rotation period, and spectral type. To do this, we examine the distributions of the flare equivalent duration on a logarithmic scale, which we consider as an indicator of the maximum energy level that a star can reach in white-light flares. We conduct these analyses using two distinct statistical models of data obtained from white-light flare patrols of 33 stars. These models are the one-phase exponential association (OPEA) and cumulative flare frequency models. The results show that the value of the OPEA model parameter Plateau has a linear relationship with the stellar B – V colour index and the rotation period. In addition, it is shown that flare time-scales, and therefore the maximum magnetic loop height that stars can have, vary according to the evolutionary stage of the star. Finally, it is concluded that the cumulative flare frequency parameters of the stars are not as effective as the OPEA parameters at exhibiting these variations.

Preprocessing Experience-Sampling-Method Data: A Step-by-Step Framework, Tutorial Website, R Package, and Reporting Templates

Experience-sampling-method (ESM) studies have become a very popular tool to gain insight into the dynamics of psychological processes. Although the statistical modeling of ESM data has been widely studied, the preprocessing steps that precede such modeling have received relatively limited attention despite being a challenging phase. At the same time, adequate preprocessing of ESM data is crucial: It provides valuable information about the quality of the data and, importantly, helps to resolve issues in the data that may compromise the validity of statistical analyses. To support researchers in properly preprocessing ESM data, we have developed a step-by-step framework, a tutorial website that provides a gallery of R code, an R package, and templates to report the preprocessing steps. Particular attention is given to three different aspects in preprocessing: checking adherence to the study design (e.g., whether the momentary questionnaires were delivered according to the sampling scheme), examining participants’ response behaviors (e.g., compliance, careless responding), and describing and visualizing the data (e.g., examining distributions of variables).

Statistics in Phonetics

Phonetics is the scientific field concerned with the study of how speech is produced, heard, and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this article, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics, speech inversion, sound change, and speech technology), an overview of the statistical methods for analyzing their data, and an introduction to the signal processing methods commonly applied to speech recordings. A major transition in the statistical modeling of phonetic data has been the shift from fixed effects to random effects regression models, the modeling of curve data (for instance, via generalized additive mixed models or functional data analysis methods), and the use of Bayesian methods. This shift has been driven in part by the increased focus on large speech corpora in phonetics, which has arisen from machine learning methods such as forced alignment. We conclude by identifying opportunities for future research.

MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of “true” redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration, and the wider applicability of PZFlow for statistical modeling of any tabular data.

Modeling Spatiotemporal Heterogeneity of Customer Preferences With Small-Scale Aggregated Data: A Spatial Panel Modeling Approach

Abstract Customer preferences are found to evolve over time and correlate with geographical locations. Studying the spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for understanding the trend of customer preferences. However, existing choice models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. Learning-based spatiotemporal data modeling methods usually require large-scale datasets for model training, which are not applicable to small aggregated data, such as the sale records of a product in several regions and years. To fill this research gap, we propose a spatial panel modeling approach to investigate the spatiotemporal heterogeneity of customer preferences. Product and regional attributes varying in time are included as model inputs to support demand forecasting in engineering design. With case studies using the dataset of small SUVs and compact sedans in China's automotive market, we demonstrate that the spatial panel modeling approach outperforms other statistical spatiotemporal data models and non-parametric regression methods in goodness of fit and prediction accuracy. We also illustrate a potential design application of the proposed approach in a portfolio optimization of two vehicles from the same producer. While the spatial panel modeling approach exists in econometrics, applying this approach to support engineering decisions by considering spatiotemporal heterogeneity and introducing engineering attributes in demand forecasting is the contribution of this work. Our paper is focused on presenting the approach rather than the results per se.

The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population.

Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO=0.65) was higher in the recipient population than predicted had no augmentation occurred (HO=0.58) but less than what was predicted by our model (HO=0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype-environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.

Совершенствование процесса принятия управленческих решений в сельском хозяйстве с применением систем искусственного интеллекта

Abstract. The problem of the quality of managerial decisions is one of the most acute problems of agriculture. Their quality can be improved with the use of digital technologies, including the use of artificial intelligence (AI) systems. The purpose of the study is to clarify the main stages of managerial decision-making, taking into account the use of AI systems. The scientific novelty lies in the development of a structural model for making a managerial decision, taking into account the use of AI systems, the main components of this process are identified. The research methods were the analysis of publications in the WoS scientific citation network on the topics “agriculture” and “artificial intelligence”, as well as the abstract-logical method in the analysis of the main stages of making a managerial decision. The results of the study were the determination of the composition and content of the stages of the procedural decision invariant, taking into account the use of artificial intelligence systems. The use of artificial intelligence systems allows diagnosing the occurrence of problems in crop production, animal husbandry, and technical systems at an early stage. Data collection and analysis in the process of making a managerial decision using AI systems includes direct data collection using sensors, cameras, scanners, etc., their cleaning and preliminary analysis, exploratory and statistical analysis, data modeling and interpretation of results. The use of AI systems will make it possible to operate with large data sets from agricultural production facilities, which will reduce uncertainty in making managerial decisions. The analysis of alternatives and the development of a management decision using AI systems turns off the forecasting of agricultural development indicators in a given system of constraints, the generation of alternative solutions and the choice of the optimal alternative, the acceptance or ignoring of the proposed alternatives. AI systems can be used to automate and optimize the process of implementing management decisions, monitoring and controlling management decisions. The use of AI systems to automate management decision-making processes in agriculture can help improve management efficiency.

Minimum density power divergence estimation for the generalized exponential distribution

Statistical modeling of rainfall data is an active research area in agro-meteorology. The most common models fitted to such datasets are exponential, gamma, log-normal, and Weibull distributions. As an alternative to some of these models, the generalized exponential (GE) distribution was proposed by Gupta and Kundu (2001a). Rainfall (specifically for short periods) datasets often include outliers, and thus, a proper robust parameter estimation procedure is necessary. Here, we use the popular minimum density power divergence estimation (MDPDE) procedure developed by Basu et al. (1998) for estimating the GE parameters. We derive the analytical expressions for the estimating equations and asymptotic distributions. We analytically compare MDPDE with maximum likelihood estimation in terms of robustness, through an influence function analysis. Besides, we study the asymptotic relative efficiency of MDPDE analytically for different parameter settings. We apply the proposed technique to some simulated datasets and two rainfall datasets from Texas, United States. The results indicate superior performance of MDPDE compared to the other existing estimation techniques in most of the scenarios.

Development and Evaluation of Statistical Models for Network Data, Such as Social Networks, Biological Networks and Brain Networks in Vietnam

Development and Evaluation of Statistical Models for Network Data, Such as Social Networks, Biological Networks and Brain Networks in Vietnam

The variation of growth in diameter and its relationship with the height of pedunculated oak (Quercus Robur L.) trees in the provenance cultures

In provenance cultures, studies were conducted on diameter growth dynamics in pedunculated oak (Quercus robur L.) trees and the height-diameter relationship was evaluated based on three data approximation models. The studies highlighted a signifi cant variation of the trunk diameter in trees of different origins and a similar growth reaction of oaks from four provenances (Hârjauca, Plaiul Fagului, Zloti, and Baimaclia) in relation to the environmental conditions of the common stand. In all the years of observation, the trees from Edineț had a rapid growth in diameter, thus demonstrating their ability to adapt through phenotypic plasticity. An uneven distribution of tree diameters was observed by provenance, indicating that dominant trees have an advantage in competition for light, slowing the growth of those with smaller diameters and causing a positive skew towards oaks with larger diameters. With the advancing age of the oaks, a tendency to decrease the values of the heritability coefficients for the trunk diameter was observed. At the age of 5 years, studies show a strong genetic control over the growth in diameter, a fact highlighted by the heritability coefficient value of 0,53 for this character. As the trees age, non-genetic factors such as environmental conditions become increasingly important in determining tree diameter growth. The comparative analysis of three statistical fitting models for data on pedunculate oak trees from different provenances, showed that the logarithmic model is the most appropriate to explain the relationship between their height and diameter.

Analysis of the competitiveness of higher education institutions in Ukraine in the context of recovery and development after the war

The article examines the challenges that arise before the education system in the conditions of military aggression. The changing political and social situation during the conflict seriously affects educational institutions and their ability to provide quality education. The purpose of the study is to create a structural and functional set of criteria for ensuring the processes of forming the competitiveness of educational institutions in the conditions of full-scale military aggression and post-war reconstruction. The article analyzes the effects of military conflict on education infrastructure, access to education, and the psychological well-being of students and staff. She reveals the need to adapt the criteria for assessing the competitiveness of educational institutions pays attention to the flexibility of the educational process and the ability to respond to emergency situations. The article used the method of literature analysis, which made it possible to systematize the results of the analysis of scientific studies, articles, reports, and other sources of information related to the competitiveness of educational institutions in the conditions of military aggression and post-war reconstruction. This allows you to establish the presence of problems, identify factors and determine previous theoretical approaches to the assessment of competitiveness. Analytical methods, including statistical data analysis, modeling, and other analytical approaches for assessing competitiveness, are used to identify relationships and important factors affecting the competitiveness of an educational institution. Based on the use of these methods, a model for assessing the competitiveness of educational institutions was developed. Using the competitive assessment model helps higher education institutions improve their strategic plans. This allows them to identify their strengths and weaknesses, identify competitive opportunities and threats, and develop effective development strategies. The competitiveness assessment model contributes to the improvement of the quality of education.

Evaluating the Discrete Generalized Rayleigh Distribution: Statistical Inferences and Applications to Real Data Analysis

Various discrete lifetime distributions have been observed in real data analysis. Numerous discrete models have been derived from a continuous distribution using the survival discretization method, owing to its simplicity and appealing formulation. This study focuses on the discrete analog of the newly generalized Rayleigh distribution. Both classical and Bayesian statistical inferences are performed to evaluate the efficacy of the new discrete model, particularly in terms of relative bias, mean square error, and coverage probability. Additionally, the study explores different important submodels and limiting behavior for the new discrete distribution. Various statistical functions have been examined, including moments, stress–strength, mean residual lifetime, mean past time, and order statistics. Finally, two real data examples are employed to evaluate the new discrete model. Simulations and numerical analyses play a pivotal role in facilitating statistical estimation and data modeling. The study concludes that the discrete generalized Rayleigh distribution presents a notably appealing alternative to other competing discrete distributions.

Um estudo tipológico baseado nos atributos dos trabalhadores(as) da economia solidária para o aperfeiçoamento das políticas públicas de geração de trabalho e renda

Abstract This article presents a typological study based on the personal attributes and economic activities of associates of solidarity economy enterprises. The main focus is on the different social actors that integrate the different types of solidarity initiatives, intending to demonstrate their multiple demands and challenges. We used Multiple Factor Analysis (MFA) with variables extracted from the sample survey of the partners of the solidary economy, carried out by Senaes, in 2013, in the five regions of Brazil. MFA allowed summarizing and hierarchizing the results of the statistical data modeling, defining the existence of common factors and dissimilarity among the 2,895 respondents. The formed clusters resulted in three types, named family farmers (type 1), traditional peoples and communities (type 2), and associated urban workers (type 3). The characterization of the types and their demands may constitute a tool to provide subsidies to formulators and executors of public policies for job and income generation since it allows the targeting of actions that reflect the needs and specificities of each grouping.

General spatial model meets adaptive shrinkage generalized moment estimation: Simultaneous model and moment selection

Spatial data are widely used in various scenarios of life and are highly valued, and their analysis and research have achieved remarkable results. Spatial data have spatial effects and do not satisfy the assumption of independence; thus, the traditional econometric analysis methods cannot be directly used in spatial models, and the spatial autocorrelation and spatial heterogeneity of spatial data make the research more complicated and difficult. Generalized moment estimation(GMM) is a powerful tool for statistical modeling and inference of spatial data. Considering the case where there is a set of correctly specified moment conditions and another set of possibly misspecified moment conditions for spatial data, this paper proposes a GMM shrinkage method to estimate the unknown parameters for spatial autoregressive model with spatial autoregressive disturbances. The proposed GMM estimators are shown to enjoy oracle properties; i.e., it selects the valid moment conditions consistently from the candidate set and includes them into estimation automatically. The resulting estimator is asymptotically as efficient as the GMM estimator based on all valid moment conditions. Monte Carlo studies show that the method works well in terms of valid moment selection and the finite sample properties of its estimators.

Design of a Cognitive Knowledge Representation Model to Assess the Reasoning Levels of Primary School Children

In recent days, the research on student’s intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analyzing the intelligence level of the learners. Each learner’s knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child’s learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student’s intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student’s reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system’s performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilisation are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customised teaching easier; hence, this article involves determining a student’s cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorises students’ cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students.

Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells.

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions. Here, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused RNAPs. In an extensive series of simulations, we show that this model enables accurate estimation of initiation and pause-escape rates. Furthermore, we show by simulation and analysis of real data that pause-escape is often strongly rate-limiting and that steric hindrance can dramatically reduce initiation rates. Our modeling framework is applicable to a variety of inference problems, and our software for estimation and simulation is freely available.

Statistical Analysis and Distribution Modeling of Rainfall Data in Flood-Prone Regions of Karnataka State

This research paper presents a comprehensive analysis of southwest monsoon rainfall data in flood-prone regions of Karnataka State, employing suitable statistical techniques and data distribution modeling. The detailed analysis of the dataset helped in understanding rainfall patterns, which is essential in identifying flood risks in Karnataka.The analysis includes the computation of descriptive statistics, exploration of data stationarity and the application of distribution models to characterize rainfall patterns. Maximum likelihood estimator has been used to estimate the parameters involved in the data distribution modelling. Further, hypothesis tests were conducted to assess the goodness-of-fit of these distribution models. These models are found to be suitable for the dataset in comparison with the Gaussian models.

Statistical Modeling of Financial Data with Skew-Symmetric Error Distributions

Based on corporate financial data for almost all companies listed on the Prime Market of the Tokyo Stock Exchange in fiscal year 2021, we gradually refine a model to explain firms’ sales by the number of employees and total assets. Starting from a Cobb–Douglas-type functional form linearized by a log transformation, the assumption of a skew-symmetric distribution in the error structure and the introduction of industry dummies are shown to be useful not only in searching for a good-fitting model, but also in ensuring the accuracy of important parameters such as the labor share. The introduction of industry dummies helps to improve the accuracy of the model as well as to allow for interpretation as sector-wise total factor productivity.

Genetic insights into the age-specific biological mechanisms governing human ovarian aging

There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.