Hierarchical Modeling Approach Research Articles

Linking Climate Variables to Large-Scale Spatial Pattern and Risk of Citrus Huanglongbing: A Hierarchical Bayesian Modeling Approach.

Huanglongbing (HLB) is one of the most important diseases affecting citriculture in the world. Knowledge of climatic factors linked to HLB risk at large spatial scales is limited. We gathered HLB presence and absence data from official surveys conducted in the state of Minas Gerais, Brazil, over 13 years. The total count of orange and mandarin orchards, and mean orchard area, normalized to a spatial grid of 60 cells (55 × 55 km), were derived from the same database. Monthly climate normals (1984 to 2013) of rainfall, mean temperature, and wind speed split into rainy (September to April) and dry (May to August) seasons (annual summary was retained) were obtained for each grid cell. Two hierarchical Bayesian modeling approaches were evaluated, both based on the integrated nested Laplace approximation method. The first, the climate covariates model (CC model), used orchard, climate, and the spatial effect as covariates. The second, principal components (PC model), used the first three components from a principal component analysis of all variables and the spatial effect as covariates. Both models showed an inverse relationship between posterior prevalence and grid cell mean temperature during the dry season. Annual wind speed, as well as annual and rainy season rainfall, contributed to HLB risk in the CC and PC models, respectively. A partial influence of neighboring regions on HLB risk was observed. The results should assist policymakers in defining regions at HLB risk and guide monitoring strategies to mitigate further spread of HLB in the state of Minas Gerais.

Accounting for Methane Dynamics in the Upper Yangtze River Valley Dammed Reservoir in China: A Hierarchical Bayesian Modeling Approach

Accounting for Methane Dynamics in the Upper Yangtze River Valley Dammed Reservoir in China: A Hierarchical Bayesian Modeling Approach

'Candidatus Liberibacter' Pathosystems at the Forefront of Agricultural and Biological Research Challenges.

'Candidatus Liberibacter' Pathosystems at the Forefront of Agricultural and Biological Research Challenges.

Operational evaluation of the bus rapid transit system: Case study of Dar es Salaam city

This study evaluated the effectiveness of the first Bus Rapid Transit (BRT) system in Dar es Salaam. A Bayesian Hierarchical modeling approach and Bayesian hypothesis test were used to compare the operational performance of BRT against the non-BRT buses and private automobiles. The study also examined factors affecting the travel times of the three modes of transportation. The study corridor, which is 10.2 kilometers long, is an arterial segment along Morogoro Road. The predicted travel time for BRT was lowest at the 95% Bayesian credible interval, with a mean value of 16 minutes. Non-BRT bus trips had the highest travel time of 28 minutes, followed by personal automobile trips with an average travel time of 18 minutes, indicating that BRT buses improve travel time along the study corridor. Factors such as delay, time of day, dwelling time, number of stops, and trip direction significantly influenced travel time variability along the study routes.

Do Attentional Lapses Account for the Worst Performance Rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

Risk investigation in circular economy: a hierarchical decision model approach

ABSTRACT Integrating circular economy practices into the conventional supply chain is a challenging task due to the sheer number of risks and uncertainties involved. These risks can hamper the functioning of recovery processes and act as impediments to the adoption of circular systems. Furthermore, there is a scarcity of studies focusing on the risk aspects in the context of the circular economy. This study attempts to address this gap in the literature by identifying the risks associated with circular economy operations and proposing a taxonomy for the same. Furthermore, a risk hierarchical decision model is developed to determine the relationships among the risks and differentiate between driving and dependent risks. A two-phase approach, kappa statistical analysis and interpretive structural modelling, is used to validate the proposed taxonomy and understand the risk interactions. The study statistically categorises the circular economy risks into different categories and scientifically evaluates the interdependencies among the risks. Unlike conventional perception, majority of the high-priority risks are organisational rather than financial in nature.

Do You Really Want to Be Forever Young? Emotional Health and Psychosocial Well-being by Relative Birth Quarter in Canadian Adolescents

PurposeThose born earlier within cohorts of similarly aged children tend to outperform peers in athletic and academic pursuits. Subsequent evidence suggests relatively younger children may also experience worse emotional and psychosocial health; however, evidence from middle adolescents is limited. This study assessed whether depression, anxiety, and psychosocial well-being differed by the relative birth quarter (RBQ) within a cohort of grades 9–12 in secondary schools across Canada. MethodsData from the 2018–19 student self-report surveys of the cannabis use, obesity, mental health, physical activity, alcohol use, smoking, and sedentary behavior study were analyzed. Students having made normative progress were categorized into RBQs by the provincial enrollment cutoff date (n = 49,921). Hierarchical modeling approaches assessed whether self-reported depression, anxiety, psychosocial well-being, and positive controls differed by RBQ after controlling for covariates. ResultsDespite relative age effects for positive control outcomes, no significant difference was detected for anxiety and depression scores or likelihood of meeting thresholds for having clinically relevant symptoms. However, a significant difference emerged between RBQs for psychosocial well-being scores; post hoc tests found that psychosocial well-being scores were lower in the fourth RBQ than those in all prior quarters. DiscussionResults agree with limited findings that relative age differences in emotional health are not significant in older cohorts. Nonetheless, relatively older individuals reported very small (d = .04–.05) advantages in psychosocial well-being than their youngest peers, which aligns with previous data. Longitudinal approaches to assess relative age effects on mental health during and throughout the transition into middle adolescence are warranted.

Spatial and temporal effects improve Bayesian price estimation for the small-scale shrimp fishery in Sergipe State, Brazil

Local shrimp productivity and economic infrastructure may vary among small fishery communities, which can lead to unequal conditions for fishers. Consequently, the domestic market value for shrimp landing can be different and decentralized in space and time. Small-scale fisheries (SSF) have several characteristics, including the spatial and temporal interactions, which add uncertainty to fishery statistics. Bayesian hierarchical models allow parameters to vary on several levels using random effects or other means of randomization, which leads to better estimation of multilevel uncertainty compared to other methods. This study tests the influence of space and time on shrimp production prices in Sergipe State, Brazil, using a Bayesian hierarchical modelling approach. We also tested whether there is a relationship between capture per unit effort and production value. We described landings of Litopenaeus schmitti and Xiphopenaeus kroyeri in 27 locations in the State of Sergipe between 2010 and 2016. Shrimp production (kg) remained relatively stable (200–300 ton/year). Using the Bayesian approach, we found that prices of both species varied among landing points within and between years, and this variation has spatial and temporal dependence. The model enhanced our understanding of which factors affect price variability in landings. In particular, our models indicated that catch per unit of effort, location, and time affected the price variability in coastal landings within the State of Sergipe. However, the seasonal monthly effects were not as important as the yearly effects, since the variance within years was found to be low. This could indicate that economic activities (tourism) do not play an important role for shrimp prices in this region. Yet, biological factors (abundance and reproduction period) can affect the prices for some locations. Improving the estimates by using methods that can account for the human dimension of fishing activities is paramount in its management, enhancing the decision-making process.

Using Radiative Convective Equilibrium to Explore Clouds and Climate in the Community Atmosphere Model

AbstractCharacteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts.

Community modeling reveals the importance of elevation and land cover in shaping migratory bird abundance in the Andes.

The tropical Andes are characterized by extreme topographic and climatic complexity, which has likely contributed to their outstanding current species diversity, composed of many range‐restricted species. However, little is known about how the distribution and abundance of highly mobile organisms, like long‐distance migratory birds, varies across different land covers, elevations, and climatic conditions within the Andes. We conducted 1,606 distance‐sampling point counts across the Colombian Andes, spanning elevations from 253 to 3,708 m, a range of precipitation regimes and representative land covers. We then employed a novel application of a multispecies hierarchical modeling approach to evaluate how elevation, local land cover, aboveground woody biomass, cloud cover, precipitation, and seasonality in precipitation shape the abundance of the migratory land bird community in the Andes. We detected 1,824 individuals of 29 species of migratory land birds, six of which were considered incidental in our study region. We modeled the abundance of the remaining 23 species, while considering observer and time of day effects on detectability. We found that both elevation and land cover had an overriding influence on the abundance of migratory species across the Andes, with strong evidence for a mid‐elevation peak in abundance, and species‐specific responses to both variables. As a community, migratory birds had the highest mean abundance in shade coffee plantations, secondary forest, and mature forest. Aboveground woody biomass did not affect the abundance of all species as a group, but a few showed strong responses to this variable. Contrary to predictions of a positive correlation between abundance and precipitation, we found no evidence for community‐level responses to precipitation, aside for a weak tendency for birds to select areas with intermediate levels of precipitation. This novel use of a multispecies model sheds new light on the mechanisms shaping the winter distribution of migratory birds and highlights the importance of elevation and land cover types over climatic variables in the context of the Colombian Andes.

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

Mapping cortico-subcortical sensitivity to 4 Hz amplitude modulation depth in human auditory system with functional MRI

Temporal modulations in the envelope of acoustic waveforms at rates around 4 Hz constitute a strong acoustic cue in speech and other natural sounds. It is often assumed that the ascending auditory pathway is increasingly sensitive to slow amplitude modulation (AM), but sensitivity to AM is typically considered separately for individual stages of the auditory system. Here, we used blood oxygen level dependent (BOLD) fMRI in twenty human subjects (10 male) to measure sensitivity of regional neural activity in the auditory system to 4 Hz temporal modulations. Participants were exposed to AM noise stimuli varying parametrically in modulation depth to characterize modulation-depth effects on BOLD responses. A Bayesian hierarchical modeling approach was used to model potentially nonlinear relations between AM depth and group-level BOLD responses in auditory regions of interest (ROIs). Sound stimulation activated the auditory brainstem and cortex structures in single subjects. BOLD responses to noise exposure in core and belt auditory cortices scaled positively with modulation depth. This finding was corroborated by whole-brain cluster-level inference. Sensitivity to AM depth variations was particularly pronounced in the Heschl's gyrus but also found in higher-order auditory cortical regions. None of the sound-responsive subcortical auditory structures showed a BOLD response profile that reflected the parametric variation in AM depth. The results are compatible with the notion that early auditory cortical regions play a key role in processing low-rate modulation content of sounds in the human auditory system.

Factors Influencing Large Real Estate Companies’ Competitiveness: A Sustainable Development Perspective

Strict policy control and real estate market downturn affects large-scale real estate enterprises performance. We surveyed large Chinese real estate enterprises and the internal factors that affect their competitiveness. Verified by the hierarchical regression and structural equation modelling approach, the results mainly show that profitability, capital ability, management and operation ability, human resource ability, brand name, and innovation ability play positive roles in the competitiveness of large real estate enterprises. Management and operation ability plays an intermediary role between human resources and the improvement of competitiveness. Real estate enterprises’ capital sources play an intermediary role between brand names and the improvement of competitiveness. Moreover, landbank area and quality and sales are three major factors that impact the competitiveness improvement of real estate enterprises, while the ability for marketing innovation and the payment collection of enterprises has a relatively small impact. All in all, this paper provides practical implications concerning factors that affect the competitiveness of large real estate enterprises. The findings are helpful to improve the sustainable development of real estate enterprises in the future. As research on factors that affect large-scale real estate enterprises is scarce, this study aims to fill this gap.

A new item response theory model for rater centrality using a hierarchical rater model approach.

Rater centrality, in which raters overuse middle scores for rating, is a common rater error which can affect test scores and subsequent decisions. Past studies on rater errors have focused on rater severity and inconsistency, neglecting rater centrality. This study proposes a new model within the hierarchical rater model framework to explicitly specify and directly estimate rater centrality in addition to rater severity and inconsistency. Simulations were conducted using the freeware JAGS to evaluate the parameter recovery of the new model and the consequences of ignoring rater centrality. The results revealed that the model had good parameter recovery with small bias, low root mean square errors, and high test score reliability, especially when a fully crossed linking design was used. Ignoring centrality yielded poor item difficulty estimates, person ability estimates, rater errors estimates, and underestimated reliability. We also showcase how the new model can be used, using an empirical example involving English essays in the Advanced Placement exam.

Trial and error: A hierarchical modeling approach to test-retest reliability

The concept of test-retest reliability indexes the consistency of a measurement across time. High reliability is critical for any scientific study, but specifically for the study of individual differences. Evidence of poor reliability of commonly used behavioral and functional neuroimaging tasks is mounting. Reports on low reliability of task-based fMRI have called into question the adequacy of using even the most common, well-characterized cognitive tasks with robust population-level effects, to measure individual differences. Here, we lay out a hierarchical framework that estimates reliability as a correlation divorced from trial-level variability, and show that reliability tends to be underestimated under the conventional intraclass correlation framework through summary statistics based on condition-level modeling. In addition, we examine how reliability estimation between the two statistical frameworks diverges and assess how different factors (e.g., trial and subject sample sizes, relative magnitude of cross-trial variability) impact reliability estimates. As empirical data indicate that cross-trial variability is large in most tasks, this work highlights that a large number of trials (e.g., greater than 100) may be required to achieve precise reliability estimates. We reference the tools TRR and 3dLMEr for the community to apply trial-level models to behavior and neuroimaging data and discuss how to make these new measurements most useful for future studies.

Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany

Estimation of abundance with wide spatiotemporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a hierarchical Bayesian modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon (Salmo salar) populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates, and fishing effort. Additional data of total or partial abundance collected in four closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied.

Methods for the inclusion of real-world evidence in network meta-analysis

BackgroundNetwork Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod.MethodsA range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior.ResultsWhilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. ‘Power prior’ NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty.ConclusionThe ‘power prior’ method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

From Efficiency Analyses to Policy Implications: a Multilevel Hierarchical Linear Model Approach

This paper examines the key factors that influenced the cost efficiency of 7,633 Vietnamese manufacturing firms during 2010–2016 via a hierarchical linear modelling (HLM) approach. The main reason for using HLM in this case is that observations in the same group may not be independent from each other (e.g. firms operate within the same city), and some variables may not vary across those observations. Although most of the findings are consistent with previous studies, the statistical power of our HLM model is higher than that of the traditional single-level analysis, suggesting that HLM can provide better analytical insights. The results further indicate a case for cities or provinces pursuing different policies aimed at improving the performance of their local firms.

Excess momentum or excess inertia: Do companies adopt technologies at the right time?✰,✰✰,★,★★

Drawing on the literature on organizational change, technological change, and inertia, this paper explores how the moment that companies choose to initiate a technological change relative to other companies from the same regional and industrial context influences the company's performance. In particular, we test the excess inertia and excess momentum phenomena that refer to timely and untimely technological shifts in companies. A data set comprising about 1000 of the largest Russian companies, affiliated with 19 industries, located in most of the Russian regions, for 10 years starting from 2008, is used. Applying a multi-level approach of hierarchical linear modeling, we estimated the region environment effect and the industry effect on sales and productivity. The use of moderation effects of the correspondent technology adoption with the average lag or lead from the representative company in the industry or region, could help us demonstrate what digital technologies are probably associated with the excess inertia and the excess momentum phenomena on the industry and regional level. The results reveal that the industry effect is a major determinant of firm productivity, whereas sales are mainly influenced by the region effect. Our investigation also found that companies are more likely to exhibit excess inertia rather than excess momentum.

Spatial and unobserved heterogeneity in consumer preferences for adoption of electric and hybrid vehicles: A Bayesian hierarchical modeling approach

The transition to low carbon vehicles known as alternative fuel vehicles (AFVs) is well underway. This transition has been motivated partly by consumer demand and partly by legislation such as the Zero Emission Vehicle mandate, which requires manufacturers to sell a certain percentage of their vehicles as AFVs. While the long-term adoption of AFVs (specifically, electric and hybrids) may take several years, there is a need to understand consumer preferences for AFV adoption and the pathways of AFV adoption from a national perspective. Therefore, this study sought to provide information about consumer preferences regarding AFV ownership while considering spatial and unobserved heterogeneity in consumer preferences, which can potentially impact societal transition to low carbon fueled vehicles. The 2017 National Household Travel Survey was used to calibrate Bayesian logit and hierarchical models. The findings of these models reveal that higher gasoline prices contribute toward the adoption of battery electric vehicles. The results also reveal that the perceived disadvantages of AFVs for long commutes are the key barrier in wider adoption of AFVs. Interestingly, frequent use of the internet by consumers revealed a higher likelihood for purchase of hybrid vehicles. Furthermore, West Coast residents are observed to be a large portion of the early adopters and are more likely to purchase hybrids as compared to battery electric vehicles. The knowledge generated by this study has implications for making better informed decisions about AFV adoption and developing incentives to promote wider adoption of AFVs by overcoming their perceived disadvantages.