Advances In Statistical Modeling Research Articles

Permafrost Carbon: Progress on Understanding Stocks and Fluxes Across Northern Terrestrial Ecosystems

AbstractSignificant progress in permafrost carbon science made over the past decades include the identification of vast permafrost carbon stocks, the development of new pan‐Arctic permafrost maps, an increase in terrestrial measurement sites for CO2 and methane fluxes, and important factors affecting carbon cycling, including vegetation changes, periods of soil freezing and thawing, wildfire, and other disturbance events. Process‐based modeling studies now include key elements of permafrost carbon cycling and advances in statistical modeling and inverse modeling enhance understanding of permafrost region C budgets. By combining existing data syntheses and model outputs, the permafrost region is likely a wetland methane source and small terrestrial ecosystem CO2 sink with lower net CO2 uptake toward higher latitudes, excluding wildfire emissions. For 2002–2014, the strongest CO2 sink was located in western Canada (median: −52 g C m−2 y−1) and smallest sinks in Alaska, Canadian tundra, and Siberian tundra (medians: −5 to −9 g C m−2 y−1). Eurasian regions had the largest median wetland methane fluxes (16–18 g CH4 m−2 y−1). Quantifying the regional scale carbon balance remains challenging because of high spatial and temporal variability and relatively low density of observations. More accurate permafrost region carbon fluxes require: (a) the development of better maps characterizing wetlands and dynamics of vegetation and disturbances, including abrupt permafrost thaw; (b) the establishment of new year‐round CO2 and methane flux sites in underrepresented areas; and (c) improved models that better represent important permafrost carbon cycle dynamics, including non‐growing season emissions and disturbance effects.

Editorial to the special issue on “Advances in statistical modelling, methods and applications”

Editorial to the special issue on “Advances in statistical modelling, methods and applications”

Preface

The 2023 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA2023), was held successfully on August 26-28th, 2023 in Changsha, China. The contributions are the results of conference participant submissions, providing readers with a broader view of mathematical and computational science methods, ideas and tools.There are 152 participants in the AMMSA 2023 conference, in which the program includes 4 keynote reports (35 minutes), 67 oral presentations (12 mins+3 Q&A) in four sessions (advances in mathematics and theory, Statistical Modelling and Data Analysis, computational intelligence and algorithms, and mathematical modelling and in engineering and physical sciences) and 15 posters. The paper entitled “An Improved Multi-dimensional Uncertainty Quantification Method Based on DNN-DRM” won the best presentation after being reviewed by the session chair at the conference. This conference emphasizes the development of theories and applications and promotes interdisciplinary endeavours among mathematicians, statisticians, scientists, engineers and researchers from other disciplines.There are 51 papers selected from 144 submissions and included in this volume. All accepted manuscripts were peer-reviewed by 2 reviewers at least in a double-blind review process. A total of 61 reviewers participated in this conference, who provided their professional and strict comments for authors to ensure the topic and quality of the manuscripts. There are seven topic partitions, including Mathematical Modelling and Theory, Advances in Statistical Modelling and Data Analysis, Computational Intelligence and Algorithms, Mathematical Modelling in Fluid Dynamics and Mechanics, Intelligent Control and Automation, Mathematical Modelling in Optical Engineering, and Mathematical Modelling in Engineering and Physical Sciences. This proceeding constitutes an up-to-date account of principles, techniques, and tools for mathematical simulation & modelling in a wide range of research fields.This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the mentioned fields above. Building on the success of this event, we hope AMMSA2023 will be one of the most spectacular events in those areas.Thanks go to all the international committee members, who assisted with the review of the papers submitted and presented at the conference. We would like to give special thanks to Prof. Alexander G. Ramm, Prof. Weide Li, Prof. Yajun Liu and Prof. Lu Leng who gave their keynote talks. Thanks also go to the four session chairs, Prof. Rao Li, Prof. Yichuan Zhao, Prof. Hassan Zedan and Prof. Quanxin Zhu who reviewed all oral presentations as well as posters. We are grateful to everyone who has contributed to the realization of AMMSA 2023.List of Conference Committees, Organization are available in this Pdf.

Family structure, unstructured socializing, and delinquent behavior

BackgroundThe association between family structure and delinquent behavior has been studied for more than 100 years. Research on this topic has become more sophisticated with the advent of large data sets and advances in statistical modeling, but it has largely confirmed that adolescents living in single parent, stepparent, or no parent families tend to be more involved than youth from two-parent families in delinquent behaviors. This pattern has been linked to differences in economic resources, cognitive functioning, parent-child relations, and parental monitoring of adolescents. The objective of this research was to investigate whether a previously unexamined concept, unstructured socializing, mediated the association between family structure and delinquent behavior. MethodUsing data from 30 nations (n = 67,470), multilevel structural equation models (SEMs) were employed to estimate the direct and indirect effects, via unstructured socializing, of family structure on delinquent behavior. ResultsThe results furnished clear support for a mediation effect via unstructured socializing among youth living with a single parent or a mother and stepfather, but no support among those living in other family structures. ConclusionsThe association between living in a single parent family and delinquent behaviors is mediated, in large part, by unstructured socializing. Thus, programs that encourage normative activities among youth from single parent families should be encouraged as a promising method to curb delinquent behavior.

Local clinical informatics investments are required for in silico biomarker generation across the globe: lessons learned from a secondary analysis of the PROP trial

Background Advances in statistical modelling and machine learning approaches, which can be deployed locally using open source programming languages, represent a unique opportunity to improve workflows and lower costs in health care across the globe through the creation of in silico biomarkers. The goal of this study was to extract meaningful data from the publicly available Prematurity and Respiratory Outcomes Program (PROP) trial data that could help generate useful clinical diagnostic aids with minimal cost for deployment in global healthcare settings. Methods A cluster analysis of the PROP dataset was conducted. We generated a simple model using an open-source software platform that generates a growth prediction of patients born less than 30 weeks. We then obtained validation data from a Uruguayan hospital to test the capacity for deployment of the models. Results Analysis revealed two main clusters of patients in the trial, with differentiation mainly based on the clinical and anthropomorphic measurements of birth gestational age, birth weight, and head circumference. The anthropometric measurements of daily weight, birth weight, head circumference, and birth gestational age were highly correlated with respiratory dysfunction and co-morbidities We note that deviation from this predicted growth curve in PROP patients was associated with culture-proven sepsis, and may represent a more sensitive anthropomorphic biomarker than the weight percentile systems routinely used globally such as Fenton curves. We found that early deviation from our projected growth model was highly associated with patient fatality. However, over long-term predictions, models trained on PROP clinical trial patients showed significantly more error in the Uruguayan patients. Conclusions Although these prediction models built upon PROP data were not generalizable to Uruguayan patients, our data suggest that prediction models using simple anthropomorphic measurements, if trained on local patients, may be able to provide value as a low-cost in silico biomarker. We concluded that local investment in clinical informatics infrastructure is needed to train models based on locally extracted clinical data.

Causal Network Analysis

Fueled by recent advances in statistical modeling and the rapid growth of network data, social network analysis has become increasingly popular in sociology and related disciplines. However, a significant amount of work in the field has been descriptive and correlational, which prevents the findings from being more rigorously translated into practices and policies. This article provides a review of the popular models and methods for causal network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity) and potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data). It covers major models and methods for both network formation and network effects and for both sociocentric networks and egocentric networks. Lastly, this review also discusses future directions for causal network analysis.

Low household income and neurodevelopment from infancy through adolescence.

Despite advancements in the study of brain maturation at different developmental epochs, no work has linked the significant neural changes occurring just after birth to the subtler refinements in the brain occurring in childhood and adolescence. We aimed to provide a comprehensive picture regarding foundational neurodevelopment and examine systematic differences by family income. Using a nationally representative longitudinal sample of 486 infants, children, and adolescents (age 5 months to 20 years) from the NIH MRI Study of Normal Brain Development and leveraging advances in statistical modeling, we mapped developmental trajectories for the four major cortical lobes and constructed charts that show the statistical distribution of gray matter and reveal the considerable variability in regional volumes and structural change, even among healthy, typically developing children. Further, the data reveal that significant structural differences in gray matter development for children living in or near poverty, first detected during childhood (age 2.5–6.5 years), evolve throughout adolescence.

Cover Image

The cover image is based on the Advance Review Advances in statistical modeling of spatial extremes by Raphaël Huser and Jennifer L. Wadsworth., https://doi.org/10.1002/wics.1537.image

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

800 Years of Making: Early Medieval Craftworking and an 8th-Century Decline in Ireland

METHODOLOGICAL ADVANCES IN STATISTICAL MODELLING of chronological data have enabled fresh perspectives of the past to be gained from the analysis of large numbers of radiocarbon (14C) dates. A new analysis of radiocarbon dates associated with craftworking activities — handicrafts like antler-working or wood-working and metalworking technologies — from Irish sites of early medieval date is presented using a bootstrapped kernel density estimation (KDE) method. These models are used to assess whether the arrival of urbanism to Ireland in the 10th century brought about a major change to craftworking traditions, and to build a new and nuanced understanding of long-term trends at both secular and ecclesiastical sites of the period. These are shown to have differed, especially in the 8th century, at a crucial point in time. The findings add to the emerging narrative of demographic ‘decline’ from similar data-driven investigations of Ireland’s early medieval past, which pinpoint the beginning of the 8th century as a seminal moment of change. Crucially, however, they also reveal a more complex pattern of change involving a combination of processes, independent of urban development, but related to demographics, socio-economic re-organisation, changing settlement patterns, and the influence of the Church.

Space use and interactions of two introduced mesopredators, European red fox and feral cat, in an arid landscape

AbstractIntroduced mammalian predators are drivers of species decline and extinction globally. The successful management and control of introduced mammalian predators is dependent on some knowledge of space use and movements in order to target a population and monitor outcomes. In Australia, these tasks are made complex as there is more than one significant introduced mammalian predator, namely the European red fox Vulpes vulpes and feral cat Felis catus, the landscapes are vast, and individual‐level interactions between predators are little studied. The impact of these two introduced predators is large and a significant factor in the extinction of many of the country’s small‐ to medium‐sized mammals, either regionally or globally. In a three‐year study, we used high‐frequency location data, the deployment of the latest GPS tracking technologies, and recent advances in statistical modeling to examine how these two species distributed themselves in space, the degree to which individual distributions overlapped, intra‐ and interspecific interactions, and temporal patterns of activity in an arid landscape. In the absence of an apex predator, the two introduced mesopredators showed large differences in how they distribute themselves across the landscape and interact with conspecifics. The red fox mostly occupies defined territories, while most feral cats roam apparently independent of each other with occasional periods of frequent interaction with conspecifics of either sex. Intraspecific attraction was strongest in cats, while interspecific avoidance was observed in both directions. The home ranges of feral cats that were range‐resident were 3–3.5 times larger than foxes in the same landscape. Notably, we observed long‐distance movements in feral cats and some were displaced up to 164 km from their point of release. A greater portion of the feral cat population were non‐sedentary and therefore likely less amenable to local control efforts than foxes. Given the different patterns of distribution in time and space, the reliable monitoring of population trends or estimates of abundance will necessarily differ in extent, intensity, or duration for the same level of precision and/or require a different method for monitoring each population.

Multi-level longitudinal learning curve regression models integrated with item difficulty metrics for deliberate practice of visual diagnosis: groundwork for adaptive learning

Visual diagnosis of radiographs, histology and electrocardiograms lends itself to deliberate practice, facilitated by large online banks of cases. Which cases to supply to which learners in which order is still to be worked out, with there being considerable potential for adapting the learning. Advances in statistical modeling, based on an accumulating learning curve, offer methods for more effectively pairing learners with cases of known calibrations. Using demonstration radiograph and electrocardiogram datasets, the advantages of moving from traditional regression to multilevel methods for modeling growth in ability or performance are demonstrated, with a final step of integrating case-level item-response information based on diagnostic grouping. This produces more precise individual-level estimates that can eventually support learneradaptive case selection. The progressive increase in model sophistication is not simply statistical but rather brings the models into alignment with core learning principles including the importance of taking into account individual differences in baseline skill and learning rate as well as the differential interaction with cases of varying diagnosis and difficulty. The developed approach can thus give researchers and educators a better basis on which to anticipate learners' pathways and individually adapt their future learning.

Precision Psychiatry Approach to Posttraumatic Stress Response

Personalized medicine has led to important discoveries and medical innovations. For the successful translation of that progress into precision psychiatry, the complexity of mental illness and its underpinning mechanisms must be considered, and data-driven approaches are needed. Computational approaches such as machine learning are important drivers of innovation and are spurred by recent advances in statistical modeling. [ Psychiatr Ann . 2021;51(1):7–13.]

Advances in statistical modeling of spatial extremes

AbstractThe classical modeling of spatial extremes relies on asymptotic models (i.e., max‐stable orr‐Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at finite levels, empirical evidence often suggests that such asymptotic models are too rigidly constrained, and that they do not adequately capture the frequent situation where more severe events tend to be spatially more localized. In other words, these asymptotic models have a strong tail dependence that persists at increasingly high levels, while data usually suggest that it should weaken instead. Another well‐known limitation of classical spatial extremes models is that they are either computationally prohibitive to fit in high dimensions, or they need to be fitted using less efficient techniques. In this review paper, we describe recent progress in the modeling and inference for spatial extremes, focusing on new models that have more flexible tail structures that can bridge asymptotic dependence classes, and that are more easily amenable to likelihood‐based inference for large datasets. In particular, we discuss various types of random scale constructions, as well as the conditional spatial extremes model, which have recently been getting increasing attention within the statistics of extremes community. We illustrate some of these new spatial models on two different environmental applications.This article is categorized under:Data: Types and Structure > Image and Spatial DataData: Types and Structure > Time Series, Stochastic Processes, and Functional DataStatistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

Exponential random graph models for little networks

Statistical models for social networks have enabled researchers to study complex social phenomena that give rise to observed patterns of relationships among social actors and to gain a rich understanding of the interdependent nature of social ties and actors. Much of this research has focused on social networks within medium to large social groups. To date, these advances in statistical models for social networks, and in particular, of Exponential-Family Random Graph Models (ERGMS), have rarely been applied to the study of small networks, despite small network data in teams, families, and personal networks being common in many fields. In this paper, we revisit the estimation of ERGMs for small networks and propose using exhaustive enumeration when possible. We developed an R package that implements the estimation of pooled ERGMs for small networks using Maximum Likelihood Estimation (MLE), called “ergmito”. Based on the results of an extensive simulation study to assess the properties of the MLE estimator, we conclude that there are several benefits of direct MLE estimation compared to approximate methods and that this creates opportunities for valuable methodological innovations that can be applied to modeling social networks with ERGMs.

Disparate dispersal limitation inGeomalacusslugs unveiled by the shape and slope of the genetic–spatial distance relationship

Long‐term dispersal ability is a key species’ trait constraining species ranges and thus large‐scale biodiversity patterns. Here we infer the long‐term dispersal abilities of threeGeomalacus(Gastropoda, Pulmonata) species from their range‐wide genetic–spatial distance relationships. This approach follows recent advances in statistical modelling of the analogous pattern at the community level: the distance decay in assemblage similarity. While linear relationships are expected for species with high long‐term dispersal abilities, asymptotic relationships are expected for those with more restricted mobility. We evaluated three functional forms (linear, negative exponential and power‐law) for the relationship between genetic distance (computed from mitochondrialcox1sequences, n = 701) and spatial distance. Range fragmentation at present time and at the Last Glacial Maximum was also estimated based on the projection of climatic niches. The power‐law function best fit the relationship between genetic and spatial distances, suggesting strong dispersal limitation and long‐term population isolation in all three species. However, the differences in slope and explained variance pointed to disparities in dispersal ability among these weak dispersers. Phylogeographic patterns ofGeomalacusspecies are thus largely driven by the same major process (i.e. dispersal limitation), operating at different strengths. This strong dispersal limitation results in geographic clustering of genetic diversity that makes these species highly vulnerable to genetic erosion due to climate change.

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affect the performance of an algorithm, however, it is yet to be known why an algorithm is preferred over the other for a certain task. The work aims at highlighting the underlying principles of machine learning techniques and about their role in enhancing the prediction performance. The study adopts, 38 most relevant studies in the field of environmental science and engineering which have applied machine learning techniques during last 6 years. The review conducted explores several aspects of the studies such as: 1) the role of input predictors to improve the prediction accuracy; 2) geographically where these studies were conducted; 3) the major techniques applied for pollutant concentration estimation or forecasting; and 4) whether these techniques were based on Linear Regression, Neural Network, Support Vector Machine or Ensemble learning algorithms. The results obtained suggest that, machine learning techniques are mainly conducted in continent Europe and America. Furthermore a factorial analysis named multi-component analysis performed show that pollution estimation is generally performed by using ensemble learning and linear regression based approaches, whereas, forecasting tasks tend to implement neural networks and support vector machines based algorithms.

Modeling the Risk of Team Sport Injuries: A Narrative Review of Different Statistical Approaches.

Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organizations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. There are a number of methods that can be used to identify injury risk factors. However, difficulty in understanding the nuances between different statistical approaches can lead to incorrect inferences and decisions being made from data. Accordingly, this narrative review aims to (1) outline commonly implemented methods for determining injury risk, (2) highlight the differences between association and prediction as it relates to injury and (3) describe advances in statistical modeling and the current evidence relating to predicting injuries in sport. Based on the points that are discussed throughout this narrative review, both researchers and practitioners alike need to carefully consider the different types of variables that are examined in relation to injury risk and how the analyses pertaining to these different variables are interpreted. There are a number of other important considerations when modeling the risk of injury, such as the method of data transformation, model validation and performance assessment. With these technical considerations in mind, researchers and practitioners should consider shifting their perspective of injury etiology from one of reductionism to one of complexity. Concurrently, research implementing reductionist approaches should be used to inform and implement complex approaches to identifying injury risk. However, the ability to capture large injury numbers is a current limitation of sports injury research and there has been a call to make data available to researchers, so that analyses and results can be replicated and verified. Collaborative efforts such as this will help prevent incorrect inferences being made from spurious data and will assist in developing interventions that are underpinned by sound scientific rationale. Such efforts will be a step in the right direction of improving the ability to identify injury risk, which in turn will help improve risk mitigation and ultimately the prevention of injuries.

Model diagnostics and refinement for phylodynamic models.

Phylodynamic modelling, which studies the joint dynamics of epidemiological and evolutionary processes, has made significant progress in recent years due to increasingly available genomic data and advances in statistical modelling. These advances have greatly improved our understanding of transmission dynamics of many important pathogens. Nevertheless, there remains a lack of effective, targetted diagnostic tools for systematically detecting model mis-specification. Development of such tools is essential for model criticism, refinement, and calibration. The idea of utilising latent residuals for model assessment has already been exploited in general spatio-temporal epidemiological settings. Specifically, by proposing appropriately designed non-centered, re-parameterizations of a given epidemiological process, one can construct latent residuals with known sampling distributions which can be used to quantify evidence of model mis-specification. In this paper, we extend this idea to formulate a novel model-diagnostic framework for phylodynamic models. Using simulated examples, we show that our framework may effectively detect a particular form of mis-specification in a phylodynamic model, particularly in the event of superspreading. We also exemplify our approach by applying the framework to a dataset describing a local foot-and-mouth (FMD) outbreak in the UK, eliciting strong evidence against the assumption of no within-host-diversity in the outbreak. We further demonstrate that our framework can facilitate model calibration in real-life scenarios, by proposing a within-host-diversity model which appears to offer a better fit to data than one that assumes no within-host-diversity of FMD virus.

Editorial for the Special Issue: “Advances in Statistical Modelling for Economic Policy-Making” in Croatian Review of Economic, Business and Social Statistics

Editorial for the Special Issue: “Advances in Statistical Modelling for Economic PolicyMaking” in Croatian Review of Economic, Business and Social Statistics