Hierarchical Model Research Articles

Hierarchical dependence modeling for the analysis of large insurance claims data

Hierarchical dependence modeling for the analysis of large insurance claims data

Semiparametric bivariate hierarchical state space model with application to hormone circadian relationship

Semiparametric bivariate hierarchical state space model with application to hormone circadian relationship

A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial census data

A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial census data

A hierarchical spline model for correcting and hindcasting temperature data

A hierarchical spline model for correcting and hindcasting temperature data

Efficient and effective calibration of numerical model outputs using hierarchical dynamic models

Efficient and effective calibration of numerical model outputs using hierarchical dynamic models

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids

One of the problems that are associated to power systems is islanding condition, which must be rapidly and properly detected to prevent any negative consequences on the system's protection, stability, and security. This paper offers a thorough overview of several islanding detection strategies, which are divided into two categories: classic approaches, including local and remote approaches, and modern techniques, including techniques based on signal processing and computational intelligence. Additionally, each approach is compared and assessed based on several factors, including implementation costs, non-detected zones, declining power quality, and response times using the analytical hierarchy process (AHP). The multi-criteria decision-making analysis shows that the overall weight of passive methods (24.7%), active methods (7.8%), hybrid methods (5.6%), remote methods (14.5%), signal processing-based methods (26.6%), and computational intelligent-based methods (20.8%) based on the comparison of all criteria together. Thus, it can be seen from the total weight that hybrid approaches are the least suitable to be chosen, while signal processing-based methods are the most appropriate islanding detection method to be selected and implemented in power system with respect to the aforementioned factors. Using Expert Choice software, the proposed hierarchy model is studied and examined.

The distinct affect regulation functions of body image flexibility and inflexibility: A prospective study in adolescents and emerging adults

Body image flexibility and inflexibility are alternative ways of responding to body image threats. Affect regulation offers a framework for understanding how these responses impact health outcomes; however, research in young people is limited. This prospective study tested two potential affect regulation functions in adolescents and emerging adults (N = 351, 66.7% female) via online surveys completed five months apart. Specifically, body image flexibility and inflexibility were tested as (a) direct predictors of adaptive (body appreciation, intuitive eating) and maladaptive (muscle-building strategies, disordered eating) outcomes, and (b) moderators of the indirect effect of a social comparison threat via body appreciation. Results from hierarchical linear and conditional process models showed that body image flexibility and inflexibility served distinct affect regulation functions. Whereas body image inflexibility directly predicted adaptive and maladaptive body- and eating-related outcomes, body image flexibility protected against the negative effects of social comparison on intuitive eating via body appreciation. Findings suggest that adolescents and emerging adults could benefit from learning body image flexibility skills and reducing inflexibility to promote positive body image and prevent body- and eating-related disturbances. Future research should investigate how body image flexibility and inflexibility relate to other established threats and outcomes in diverse demographic groups.

A hierarchical signal detection model with unequal variance for binary responses.

Gaussian signal detection models with equal variance are commonly used in simple yes-no detection and discrimination tasks whereas more flexible models with unequal variance require additional information. Here, a hierarchical Bayesian model with equal variance is extended to an unequal-variance model by exploiting variability of hit and false-alarm rates in a random sample of participants. This hierarchical model is investigated analytically, in simulations and in applications to existing data sets. The results suggest that signal variance and other parameters can be accurately estimated if plausible assumptions are met. It is concluded that the model provides a promising alternative to the ubiquitous equal-variance model for binary data.

Leading Volunteers Effectively: Development and Preliminary Validation of the Volunteer Leader Behavior Scale

Volunteers are integral to civic and social life, and leadership is integral to keeping volunteers satisfied and committed. However, volunteer leadership research is hindered by the dispersion of leadership theories and the absence of a specialized leader behavior assessment tool. To address this, we developed and validated a four-dimensional scale for evaluating the behaviors of leaders of volunteers. Using confirmatory factor analyses, we confirmed a four-factor, hierarchical model in an Australian emergency volunteer sample. Concurrent validity was confirmed in relation to satisfaction with leadership, overall satisfaction with volunteering experience, and affective organizational commitment. Further details of quantitative studies for scale evaluation are provided. This study provides researchers and practitioners with a valid and reliable instrument to assess different aspects of effective leader behaviors within the volunteering context. Implications, limitations, and future research directions are discussed.

Interactions between runoff volume, timing, and annual temperatures shape migration phenology of a threatened adfluvial sucker

AbstractMigratory fish species have been particularly impacted by changes to the hydrologic and climatic cues to which migration and spawning behaviours have been adapted across generations. While conservation and recovery programs increasingly implement flow management actions to promote successful migration and spawning, uncertainty regarding how spawning migration phenology responds to changing environmental conditions can limit the ability to effectively target such recovery actions. Here, we use a Bayesian hierarchical modelling framework to analyse spawning migration phenology of individually tagged June suckers (Chasmistes liorus) – a federally threatened, long‐lived, iteroparous, adfluvial species endemic to Utah Lake (Utah, USA) and its tributaries. We then examine how annual hydrologic and thermal conditions relate to different components of annual migration phenology, including peak migration date, in‐stream residence time, and among‐individual variation in migration timing. Peak migration date occurred earlier in years with warmer spring air temperatures (a proxy for water temperatures), though this effect interacted with peak runoff timing. Both residence time and among‐individual variation in migration timing were greater in years with larger spring discharge and later peak flows. Residence time was also longer in warmer years. These results highlight how natural and anthropogenic changes to river flow and thermal regimes are likely to impact June sucker migration timing and duration, and our approach can be applied to other migratory species to identify the external drivers of the different components of migration phenology.

Estimating ethanol correction factors for δ13C and δ15N isotopic signatures of freshwater zooplankton from multiple lakes

AbstractIn freshwater systems, δ13C and δ15N stable isotopes can be used to differentiate between pelagic and littoral energy sources and to quantify trophic position. In these ecosystems, crustacean zooplankton are frequently used to characterize the pelagic baseline. Zooplankton samples are often preserved prior to processing and analysis, which can affect isotopic signatures. Variability in preservation effects across studies make it difficult to determine if and how to correct for preservation effects. Here, we develop a correction factor for ethanol preservation and present a flexible statistical method that can be updated with additional data to increase its applicability. We collected zooplankton from five lakes in Minnesota, USA encompassing wide isotopic ranges (δ13C from −37.23‰ to −23.96‰; δ15N from 3.07‰ to 14.44‰). Changes in zooplankton δ13C and δ15N signatures were quantified using a Bayesian hierarchical model predicting fresh values from ethanol‐preserved values. Ethanol preservation increased δ13C by a factor of 1.158 (95% CI 0.866–1.441) and had a negligible effect on δ15N (slope = 1.077; 95% CI 0.833–1.359). Lake‐specific values did not differ from the overall relationship. K‐fold and leave‐one‐out cross validation tests verified that both models were accurate; RMSE of predicted δ13C = 0.701 and RMSE of predicted δ15N = 0.590. Our correction factors could be applied to other systems in which baseline δ13C and δ15N values fall within the range of our study, and this approach also enables the inclusion of data from additional lakes to estimate new corrections.

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco.

Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control.

Internet-delivered cognitive-behaviour therapy for anxiety related to asthma: study protocol for a randomised controlled trial.

There is an established association between asthma and anxiety. The overlap between asthma symptoms and symptoms of anxiety may cause individuals to overestimate their asthma severity and restrict their daily activities leading to a low quality of life. There is currently weak evidence for treatments targeting anxiety related to asthma, but cognitive-behavioural therapy (CBT) has shown some promising but mixed results. The current randomised controlled trial will investigate if exposure-based internet-delivered CBT (Internet-CBT) is more effective than treatment as usual+medical education (TAU+ME) to relieve symptoms of anxiety and asthma control. 90 participants will be randomised 1:1 to 8 weeks of Internet-CBT or TAU+ME. The primary outcome, the patient-reported Catastrophising Asthma Scale, will be analysed from baseline to the primary endpoint at 16 weeks using hierarchical linear mixed model of the slope over time. Secondary outcomes, such as asthma control, quality of life and forced expiratory volume in 1 s, will be analysed correspondingly. All participants will be informed about the study and leave their consent before study entry. All results will be analysed at group level and reported through publication in a peer-reviewed scientific journal within the field. The study received ethical approval by the Swedish Ethical Review Authority in January 2020 (ID: 2019-05985; 2022-01117-02). Registered at ClinicalTrials.gov (ID: NCT04230369).

Modelling enablers of business continuity for casual dining restaurants in post-COVID-19 era: an ISM and MICMAC perspective

Purpose While the COVID-19 pandemic has given many firms the importance of business continuity, restaurants have yet to realize the essential enablers. In this regard, the study explored the potential enablers that can help firms attain business continuity in the post-COVID-19 era for casual dining restaurants. Design/methodology/approach This study used an extensive literature review and further supported 28 restaurant managers with an average experience of 8.5 years to identify the potential enablers for ensuring business continuity for casual dining restaurants. Subsequently, this study used the interpretive structural modelling coupled with Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis to understand the context-specific inter-relationships as a hierarchical structural model. Findings Findings suggest that financial capability forms the key driver for other enablers in the hierarchy of business continuity for casual dining restaurants. Furthermore, manpower (local and outstation) are the second-level drivers of restaurant process control, digital presence, online reviews and feedback management, scalability, food quality, food delivery management, employee safety and hygiene. The third-level factors further help restaurants to achieve the dynamic capability required to provide customer value and ensure business continuity. Originality/value The study is the foremost to explore a business continuity framework in the new normal for casual dining restaurants in the country. Furthermore, several contextual inter-relationships exhibiting the hierarchy are also established for the business continuity of restaurants in the new normal.

The Impact of Development on Antidepressant and Placebo Response in Anxiety Disorders: A Bayesian Hierarchical Meta-Analytic Examination of Randomized Controlled Trials in Children, Adolescents, and Adults.

Background: Understanding how development influences medication and placebo responses in anxiety disorders could inform treatment decisions, including age-specific first- versus second-line psychopharmacological interventions. Objective: To meta-analytically compare the trajectory of selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and placebo response in youth and adults with anxiety disorders. Methods: Weekly symptom severity data were extracted from prospective, randomized, parallel-group, placebo-controlled trials of SSRIs and SNRIs in children, adolescents, and adults with anxiety disorders (generalized, separation, and social anxiety disorders as well as panic disorder). Treatment response was modeled for the standardized change in continuous measures of anxiety using a Bayesian hierarchical model. Change in symptom severity was evaluated as a function of time, and post hoc analyses were conducted to determine the sensitivity of these results across sample heterogeneity and alternative functional forms. Results: Data were included from 11 trials of youth (SSRI, κ = 7; SNRI, κ = 4) and 71 studies of adults (SSRI, κ = 46; SNRI, κ = 25). In total, 1067 youth participated in SSRI trials and 1024 in SNRI trials. In total, 10,826 adults participated in SSRI trials (placebo, n = 5367; SSRI n = 5,459) and 6232 in SNRI trials (placebo, n = 3,128; SNRI n = 3,094). A logarithmic model best described the response. Placebo response was similar in youth and adults (mean difference = -1.98 ± 6.21, 95% credible interval [CrI]: -10.2 to 14.2, p = 0.750), and statistically significant improvement from baseline emerged by week 2 in both adults (mean difference: -18.34 + 1.017, 95% CrI: -20.3 to 16.3, p < 0.001) and youth (mean difference: -23.74 + 3.736, 95% CrI: -31.1 to -16.4, p < 0.001). SSRIs produced similar improvements for youth and adults (p = 0.129), but SNRIs produced slower improvement in youth than adults (p = 0.018). Conclusions: Antidepressant-related improvement occurs early in youth and adults with anxiety disorders. SSRI response is similar in adults and youth; however, SNRIs produce greater responses in adults than youth, potentially representing a developmental effect.

Constraining the LVK AGN channel with black hole spins

Abstract Merging black holes (BH) are expected to produce remnants with large dimensionless spin parameters (aspin ∼ 0.7). However, gravitational wave (GW) observations with LIGO/Virgo/Kagra (LVK) suggest that merging BH are consistent with modestly positive but not high spin (aspin ∼ 0.2), causing tension with models suggesting that high mass mergers are produced by hierarchical merger channels. Some BH also show evidence for strong in-plane spin components. Here we point out that spin down of BH due to eccentric prograde post-merger orbits within the gas of an active galactic nucleus (AGN) disk can yield BH with masses in the upper mass gap, but only modestly positive aspin, and thus observations of BH with low spin do not rule out hierarchical models. We also point out that the fraction of BBH mergers with significant in-plane spin components is a strong test of interactions between disk binary black holes (BBH) and nuclear spheroid orbiters. Spin magnitude and spin tilt constraints from LVK observations of BBH are an excellent test of dynamics of black holes in AGN disks, disk properties and the nuclear clusters interacting with AGN.

Sources of noise exposure across Australian workplaces: cross-sectional analysis and modelling the impact of a targeted noise-source reduction initiative.

Workplace noise regulations and guidance follow the hierarchy of control model that prioritizes eliminating or reducing noise at its source. To determine the main sources of workplace noise exposure in the Australian working population and estimate the reduction of workers exposed over the noise limit (LAeq,8h > 85 dB) if noise levels of specific tools or equipment were reduced by 10 dB. Information on the tools used and tasks performed during each participant's last working shift was collected from 4,977 workers via telephone survey. Using a predetermined database of task-based noise levels, partial noise exposures (Pa2h) were determined for each noisy activity performed by the workers and their daily noise exposure level (LAeq,8h) was estimated. Partial exposures were categorized into 15 tool/task groups and the tally, average, and sum (Pa2h) for each group were calculated. The impacts of 5 different scenarios that simulated a reduction of 10 dB in noise emissions for specific tool groups were modelled. Powered tools and equipment were responsible for 59.3% of all noise exposure (Pa2h); vehicles for 10.6%; mining, refineries, and plant equipment for 5.1%; and manufacturing and food processing for 4.2%. Modelling demonstrated that a 10 dBA noise-level reduction of all powered tools and equipment would lead to a 26.4% (95% confidence interval: 22.7% to 30.3%) reduction of workers with an LAeq,8h > 85 dB. This could represent over 350,000 Australian workers no longer exposed above the workplace limit daily. A universal reduction of 10 dB to power tools and equipment would substantially reduce the future burden of hearing loss, tinnitus, workplace injuries, and other health effects. Initiatives to reduce the noise emissions of specific powered tool groups are warranted.

Evaluating School Location Based on a Territorial Spatial Planning Knowledge Graph

The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning.

Cognitive influences on biosecurity measure compliance during a global pandemic

IntroductionDuring the first years of the pandemic, COVID-19 forced governments worldwide to take drastic measures to reduce the spread of the virus. Some of these measures included mandatory confinements, constant use of masks, and social distancing. Despite these measures being mandatory in many countries and the abundance of evidence on their effectiveness at slowing the spread of the virus, many people failed to comply with them.MethodsThis research explored the role of cognitive factors in predicting compliance with COVID-19 safety measures across two separate studies. Building on earlier work demonstrating the relevance of cognitive processes in health behaviour, this study aimed to identify key predictors of adherence to safety guidelines during the pandemic. Utilising hierarchical regression models, we investigated the influence of age, sex, cognitive control, cognitive flexibility (Study 1), working memory, psychological health, and beliefs about COVID-19 (Study 2) on compliance to biosafety measures.ResultsDemographic variables and cognitive control were significant predictors of compliance in both studies. However, cognitive flexibility and working memory did not improve the models’ predictive capacities. In Study 2, integrating measures of psychological health and beliefs regarding COVID-19 severity significantly improved the model. Further, interaction effects between age and other variables also enhanced the predictive value.DiscussionThe findings emphasise the significant role cognitive control, age, psychological health, and perceptions about COVID-19 play in shaping compliance behaviour, highlighting avenues for targeted interventions to improve public health outcomes during a pandemic.