What is ‘advanced statistical modelling’?: commentary on “Replication and methodological robustness in quantitative typology” by Becker and Guzmán Naranjo

This study aimed to calculate the ruin probability across different insurance lines and propose an optimal portfolio optimization method for insurance companies to enhance financial stability and minimize risk. A quantitative data analysis approach was employed using historical data from 2014 to 2023 across various insurance lines, including life, liability, freight, fire, and engineering. Statistical distribution models such as exponential and normal distributions were applied to estimate loss distributions. Portfolio optimization was performed using Markowitz’s portfolio theory, considering constraints such as Solvency II regulations and market conditions, with data analyzed using MATLAB and Excel. The results indicated that life insurance exhibited the highest ruin probability at 0.9921, while personal insurance had the lowest at 0.1447. Engineering and freight insurance also showed high ruin probabilities due to large financial exposures. Portfolio optimization significantly reduced the value at risk (VaR-99%), from 37.5% to 26.6% for liability insurance and from 27.8% to 20.3% for freight insurance, demonstrating the effectiveness of strategic capital allocation. Investments in stocks provided higher returns compared to bank deposits and real estate, with the optimal portfolio comprising 34% stocks, 39% deposits, and 27% real estate. Accurate calculation of ruin probabilities and optimal portfolio management are crucial for mitigating financial risks in insurance companies. Diversification and dynamic portfolio adjustments, supported by advanced statistical models and regulatory compliance, enhance financial stability and ensure operational efficiency in volatile markets.

This study investigated the long-term changes in climatological condition, such as temperature, rainfall, humidity, storms, and tidal surge and flooding in Barguna district of Bangladesh. The BMD (Bangladesh Meteorological Department) data were used for temperature, rainfall, and humidity. The data for storms have been collected from secondary sources i.e. various journals, website and various records. This study applied the time series statistical test to examine the long-term changes in climatological condition, such as temperatures (1970-2015), rainfall (1975-2013), humidity (1975-2013), and storms (1987-2007). A parametric technique of trend analysis was used in this study for its inclusive use and simplicity. Data screening and filling missing values were also done before the analyses. The analyses of measured temperature indices that the annual average maximum and minimum mean temperature has increased 1.16°C and 0.56°C respectively at the end of 2015 since 1982 indicating that the weather has been becoming hotter. The analysis of the last 30 years rainfall data of Barguna was compared with national total annual rainfall and no significant change observed. The number of wetter days has been increasing over the time and more rainfall has been happening within small time. There is also a significant change in relative humidity. A total of thirty-five depressions, storms and cyclones hit Barguna district during the last 130 years among which hard hit severe cyclones have occurred in 1935, 1965, 1970 and the most recent cyclone SIDR in 2007 was notable. The study found that storm surge level and surge induced coastal flooding reached from the outfall of Baleshwar River up to 50 km upstream at Morrelganj with high surge level. Therefore, this study will provide a condensed view in further studies in advanced statistical modeling in combination with future scenario analysis for micro and macro scale climate variable changes in Bangladesh.

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.

Associations of Environmental Phenols Exposure with Cognitive Function in Middle-Aged and Older Adults: A Nested Case-Control Study.

Post-disaster reconstruction projects are critical for restoring communities and infrastructure, yet they are fraught with complex risks and interdependent challenges. This study aims to identify and analyze key risk factors in post-disaster reconstruction, focusing on their interrelationships and impacts on project outcomes. The research integrates a comprehensive literature review and experts’ perceptions to identify and validate primary risk factors, with the aim of designing a hypothetical interrelationship. This study employs a mixed-methods approach, including an empirical survey to collect data from key experienced stakeholders, factor analysis (EFA), structural equation modeling (SEM), and seven critical risk factors, including resource-, environmental-, financial-, management-, socioeconomic-, technical-, and organizational-related risk factors, which are extracted, and their interrelationship model is further examined and validated using SPSS AMOS V24. A case study analysis was conducted to examine how these factors interact in real-world settings. After consulting case study recovery participants, the results indicate significant influence from identified critical risk factors in the context of the case project. While the methods offer strong insights, this study is limited by case-specific factors. Advanced statistical modeling like SEM provides detail but may not be fully generalizable due to local variations in conditions, stakeholder dynamics, and reconstruction processes. Nevertheless, by providing actionable insights and tools, this research serves as a guide to policymakers, project managers, and community leaders, helping them predict and model risks to develop appropriate strategies for improving the resilience and efficiency of future reconstruction efforts.

Chemotherapy administration and supportive management for solid tumors is intended to take place in the ambulatory setting, but little is known about why some patients experience treatment-related adverse events so severe as to require acute inpatient care. The aim of the study was to identify predictors of initial and repeated unplanned hospitalizations and potential financial impact among Medicare patients with early-stage (Stages I-III) colorectal cancer receiving outpatient chemotherapy. Advanced statistical modeling was used to analyze a cohort of patients (N = 1,485) from the Surveillance, Epidemiology, and End Results (SEER)-Medicare database diagnosed from 2003 to 2007 with colorectal cancer as their first primary malignancy. Patients were of ages 66 and older at diagnosis, had uninterrupted Medicare Parts A and B coverage with no health maintenance organization component, and received chemotherapy at least one time. Female gender, younger age, multiple comorbidities, rural geography, higher high school completion rates, and lower median income per census tract were significant predictors of the likelihood of initial unplanned hospitalizations. Non-White race, receipt of radiation therapy, rural geography, and higher weighted comorbidity scores were factors associated with the number of hospitalizations experienced. The total Medicare charges calculated for these admissions was $38,976,171, with the median charge per admission at $20,412. Demographic and clinical factors that form the foundation of work toward development of a risk factor profile for unplanned hospitalization were identified. Further work is needed to incorporate additional clinical data to create a clinically applicable model.

Transitioning from paediatric to adult healthcare is a pivotal phase for adolescents and young adults (AYAs) with chronic conditions. Effective healthcare transition (HCT) supports continued healthcare engagement and stable psychosocial outcomes, reducing risks for patients, their families, and the healthcare system. Yet the determinants of a smooth transition are complex, shaped by the interplay of various biopsychosocial constructs. The ExPeCT study (acronym: Exploring Predictors of a successful healthCare Transition) seeks to: A) examine the relationship between biopsychosocial development, transition readiness, and HCT outcomes, including patients' experiences; and B) identify key predictors of successful HCT in AYAs with chronic conditions. This longitudinal, observational, exploratory cohort study is embedded in the PROactive cohort, which annually measures biopsychosocial well-being of children with chronic conditions. The ExPeCT study supplements the PROactive cohort by collecting additional data on HCT experiences at three time points: once in paediatric care and twice after transfer to adult care.Data will be obtained from a range of chronic disease groups. Advanced statistical modelling will be employed to analyse transition outcomes and experiences, and to investigate associations between biopsychosocial development and transition readiness. This study is expected to fill a critical gap in the field by providing comprehensive understanding of predictors of successful healthcare transition in AYAs with chronic conditions. The insights gained will help improve care strategies and guide healthcare professionals in supporting successful transition to adult care.

BackgroundIn Denmark, a nationwide mandatory infliximab biosimilar-to-biosimilar switch, from CT-P13 to GP1111, was recently conducted in patients with rheumatoid arthritis (RA) to save costs (ref). It is of interest to...

Neighborhoods are increasingly studied as a context where children and youth develop; however, the extent of neighborhoods' impact remains debatable because it is difficult to disentangle this impact from that of the family context, in part because families have some choice as to where they live. Evidence from randomized experiments, studies using advanced statistical models, and longitudinal studies that control for family characteristics indicates that neighborhoods do matter. In nonexperimental studies, small to moderate associations were found, suggesting that children and adolescents living in high-income neighborhoods had higher cognitive ability and school achievement than those living in middle-income neighborhoods, and children and adolescents living in low-income neighborhoods had more mental and physical health problems than those living in middle-income neighborhoods. The home environment has been shown to be partly responsible for the link between neighborhood and children's development. For adolescents, neighborhood effects are partially accounted for by community social control. Experimental studies in which families were randomly assigned to move to low-poverty neighborhoods from housing projects found larger neighborhood effects than nonexperi-mental research, particularly for boys' outcomes. Additional issues reviewed are relevant neighborhood characteristics, theoretical models explaining the pathways underlying neighborhood effects, methods for research assessing neighborhood processes, and policy implications.

A machine learning approach to identifying pregnant women's risk for persistent post-traumatic stress following childbirth

Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.

Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration.

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. These algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of even a basic three-neuron circuit like the crab stomatogastric nervous system. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of a three-neuron circuit up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

The question of whether immune dysfunction contributes to risk of psychiatric disorders has long been a subject of interest. To assert this hypothesis a plethora of correlative evidence has been accumulated from the past decades; however, a variety of technical and practical obstacles impeded on a cause-effect interpretation of these data. With the advent of large-scale omics technology and advanced statistical models, particularly Mendelian randomization, new studies testing this old hypothesis are accruing. Here we synthesize these new findings from genomics and genetic causal inference studies on the role of immune dysfunction in major psychiatric disorders and reconcile these new data with pre-omics findings. By reconciling these evidences, we aim to identify key gaps and propose directions for future studies in the field.

Given the current climate change scenario, it is essential to find strategies to reduce environmental risks and obtain economically sustainable agricultural productions. This study investigated the impact of various agronomic treatments on an almond orchard in northeastern Portugal, focusing on their relationships with crop growth/vigour and yield. The experiment was conducted using a factorial design that combined three variables: almond cultivar (Constantí and Vairo), irrigation regime (full and regulated deficit irrigation), and kaolin application (with or without application). These combinations resulted in eight distinct treatments, each replicated across two experimental plots. To monitor the crop physiological status, two drone flights equipped with a multispectral camera were flown during the kernel-filling stage (3 and 30 August 2021). Vegetation indices (VI) derived from the multispectral images were used to assess the crop vigour. In relation to the production data, including kernel and in-shell almond weights, these were collected in 14 representative trees of each treatment. Lastly, parametric and nonparametric regression analyses were performed to better understand relationships between VI and crop yields and derive predictive models. The main results can be summarised as follows: (a) cv. Vairo was more vulnerable to the regulated deficit irrigation strategy with striking repercussions on almond production, translating into an average reduction per tree of 22% and 16% in almond kernel and in-shell almonds compared to full irrigation, respectively; (b) kaolin application did not reflect statistically significant differences in the mean crop yield, as Tukey’s pairwise comparisons involving kaolin as a differentiating factor (e.g., C100+k—C100, V100+K—V100) showed confidence intervals with central value close to zero; and (c) regression analysis using the nonparametric random forest model and individualised treatments demonstrated a better agreement with the observed data (R2 > 0.7). This research provided valuable insights into how cultivar selection, irrigation strategy, and kaolin application can influence the almond crop performance. When integrating multispectral aerial monitoring and advanced statistical modelling, it enables an effective assessment of both crop vigour and expected yield, supporting the development of more informed and adaptive management practices to face emerging environmental challenges.