Bayesian Approach Research Articles

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.

Seismic fragility analysis of simply supported bridges considering uncertainty in scour condition

Bridges are vital infrastructures but face compounding risks from scouring and seismic events. Although recent studies have explored these individual and overlapping hazards, they often utilize deterministic approaches and focus on specific bridge types in limited geographical areas. This study addresses these gaps by introducing a probabilistic framework for assessing the seismic performance of multi-span reinforced concrete (RC) highway bridges in the presence of scour-related uncertainties. A time-dependent scour model, informed by a Bayesian approach and literature-based uncertainties, is integrated into the framework. Probabilistic Seismic Hazard Analysis (PSHA) and record selection strategies are employed to define the seismic demand probabilistically. Monte Carlo simulation (MCS) is utilized to propagate combined uncertainties during the analysis, considering different scour scenarios defined according to the discharge peak condition of the flood event at the specific location of the bridge. In the last step of the probabilistic framework, fragility analyses are conducted on individual components and the entire system by means of nonlinear dynamic models and multiple stripe analysis (MSA). An actual five-span bridge with simply supported prestressed concrete girders, located in Chile, is used as application example of the proposed approach, considering measured hydrological data and seismic hazard of the real location of the bridge. The study identifies distinct vulnerabilities across various bridge components under scour conditions. Elastomeric bearings are particularly susceptible in scenarios characterized by low scour depths (low discharge peak condition). Conversely, abutments display heightened vulnerabilities as scour depth increases (medium discharge peak condition). Piles also exhibit notable vulnerability, escalating in scenarios with great scour depth (medium discharge peak condition). Notably, piles tend to be more susceptible than columns to scour effects, especially in bents featuring shorter columns. These disparities in vulnerability are influenced by the bridge design philosophy, as well as the synergistic effects of seismic forces and varying scour depths. System fragility curves further elucidate that the likelihood of exceeding specific damage thresholds varies according to different scour scenarios, with the deeper scour depth scenario, displaying the higher vulnerabilities.

Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a clinical perspective.

Automatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra- and inter-operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their uncertainty. This work aims to evaluate the uncertainty quantification provided by two Bayesian and two non-Bayesian approaches for a multi-class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC). Four uncertainty quantification approaches were implemented in this work, based on a benchmark CNN currently employed in medical image segmentation: two Bayesian CNNs with different regularizations (Dropout and DropConnect), named BDR and BDC, an ensemble method (Ens) and a test-time augmentation (TTA) method. They were compared in terms of segmentation accuracy, using the Dice score, uncertainty quantification, using the ratio of correct-certain pixels (RCC) and incorrect-uncertain pixels (RIU), and with respect to inter-observer variability in manual segmentation. They were trained with the Kidney and Kidney Tumor Segmentation Challenge launched in 2021 (Kits21), for which multi-class segmentations of kidney, RC, and cyst on 300 CT volumes are available. Moreover, they were tested considering this and other two public renal CT datasets. Accuracy results achieved large differences across the structures of interest for all approaches, with an average Dice score of 0.92, 0.58, and 0.21 for kidney, tumor, and cyst, respectively. In terms of uncertainties, TTA provided the highest uncertainty, followed by Ens and BDC, whereas BDR provided the lowest, and minimized the number of incorrect certain pixels worse than the other approaches. Again, large differences were seen across the three structures in terms of RCC and RIU. These metrics were associated with different risk propensity, as BDR was the most risk-taking approach, able to provide higher accuracy in its prediction, but failing to assign uncertainty on incorrect segmentation in every case. The other three approaches were more conservative, providing large uncertainty regions, with the drawback of giving alert also on correct areas. Finally, the analysis of the inter-observer segmentation variability showed a significant variation among the four approaches on the external dataset, with BDR reporting the lowest agreement (Dice = 0.82), and TTA obtaining the highest score (Dice = 0.94). Our outcomes highlight the importance of quantifying the segmentation uncertainty and that decision-makers can choose the approach most in line with the risk propensity degree required by the application and theirpolicy.

Impacts of financial development and green trade on the shadow economy: An insight of eagle countries using Bayesian approaches.

The existence of a shadow economy is recognized as an impediment to sustainable development. By applying the Bayesian approaches, the current article investigates the linkage between financial development, green trade, and the scope of the shadow economy, aiming to contribute to a comprehensive understanding of how these factors address the challenge posed by the shadow economy in Emerging and Growth-Leading Economies (EAGLE) from 2003 to 2016. The results demonstrate that (i) The progress of the financial sector is expected to diminish the scale of the shadow economy. Specifically, the expansion of financial institutions and markets has a strong and negative influence on the shadow economy. (ii) Increased involvement in green trade is likely to result in a decreased shadow economy. Empirical findings provide evidence for effective policymaking in simultaneously promoting sustainable trade practices, strengthening financial systems, and curtailing informal economic activities for inclusive economic development.

Assessing the survival time of hospitalized patients in Eastern Ethiopia during 2019–2020 using the Bayesian approach: A retrospective cohort study

AbstractBackground and AimsSevere acute malnutrition remains a significant health challenge, particularly in low‐ and middle‐income countries. The aim of this study was to determine the survival time of under‐five children with severe acute malnutrition.MethodsA retrospective cohort study was conducted at a hospital, focusing on under‐five children with severe acute malnutrition. The study included 322 inpatients admitted to the Chiro hospital in Chiro, Ethiopia, between September 2019 and August 2020, whose data was obtained from medical records. Survival functions were analysed using Kaplan‒Meier plots and log‐rank tests. The survival time of severe acute malnutrition was further analysed using the Cox proportional hazards model and Bayesian parametric survival models, employing integrated nested Laplace approximation methods.ResultsAmong the 322 patients, 118 (36.6%) died as a result of severe acute malnutrition. The estimated median survival time for inpatients was found to be 2 weeks. Model selection criteria favored the Bayesian Weibull accelerated failure time model, which demonstrated that age, body temperature, pulse rate, nasogastric (NG) tube usage, hypoglycemia, anemia, diarrhea, dehydration, malaria, and pneumonia significantly influenced the survival time of severe acute malnutrition.ConclusionsThis study revealed that children below 24 months, those with altered body temperature and pulse rate, NG tube usage, hypoglycemia, and comorbidities such as anemia, diarrhea, dehydration, malaria, and pneumonia had a shorter survival time when affected by severe acute malnutrition under the age of five. To reduce the death rate of children under 5 years of age, it is necessary to design community management for acute malnutrition to ensure early detection and improve access to and coverage for children who are malnourished.

EXPRESS: Do Gender-Diverse Teams Deliver Better Operational Performance: an Experimental Study

The routines-based approach to operations management emphasizes the role of routines in operational performance. However, little is known about how gender diversity in teams could alter coordination and change behaviors of team members, potentially impacting performance. In this study, we report on two laboratory experiments that induce routines to assess the stability of coordination and adaptability to organizational changes across mixed-gender teams of varying diversity and empirically examine operational performance via routines. Based on frequentist and Bayesian statistical approaches, our findings reveal that compared to other mixed-gender teams, balanced-gender teams (a) attain stability in routines faster, (b) exhibit varying levels of adaptability to internal and external changes, and (c) deliver better economic benefits via resource- and time- efficiencies under normal and change conditions, and this is attributable to their higher pace of routine formation. We find the operational-performance effect more prominent in situations of change than in normal conditions. Alongside our theoretical contributions, our study has several implications for management and effective operations management in terms of team composition, hiring, and staffing decisions.

Economic diplomacy: Analyzing theories and models for informed policy

This paper examines the intricate relationship between economic theories, trade policies, and diplomatic engagements in the context of international relations. Utilizing theoretical frameworks such as the theory of comparative advantage and the Heckscher-Ohlin model, we analyze the foundational principles guiding global trade and policy-making. We explore the role of probabilistic models, including Bayesian statistics and regression analysis, in forecasting economic trends and their implications for international relations. Furthermore, we investigate the impact of trade policies, including tariffs, non-tariff barriers, and global trade agreements, on economic dynamics and diplomatic interactions. Through case studies of economic sanctions, we assess the effectiveness of sanctions as a diplomatic tool and propose policy recommendations for their optimal implementation. Our analysis highlights the complexity of economic diplomacy and underscores the importance of quantitative analysis in informing policy decisions and fostering international cooperation.

Robust Bayesian nonparametric variable selection for linear regression

AbstractSpike‐and‐slab and horseshoe regressions are arguably the most popular Bayesian variable selection approaches for linear regression models. However, their performance can deteriorate if outliers and heteroskedasticity are present in the data, which are common features in many real‐world statistics and machine learning applications. This work proposes a Bayesian nonparametric approach to linear regression that performs variable selection while accounting for outliers and heteroskedasticity. Our proposed model is an instance of a Dirichlet process scale mixture model with the advantage that we can derive the full conditional distributions of all parameters in closed‐form, hence producing an efficient Gibbs sampler for posterior inference. Moreover, we present how to extend the model to account for heavy‐tailed response variables. The model's performance is tested against competing algorithms on synthetic and real‐world datasets.

Flexible cost-penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease.

We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data.

Dengue fever mapping in Bangladesh: A spatial modeling approach

AbstractBackgroundEpidemics of the dengue virus can trigger widespread morbidity and mortality along with no specific treatment. Examining the spatial autocorrelation and variability of dengue prevalence throughout Bangladesh's 64 districts was the focus of this study.MethodsThe spatial autocorrelation is evaluated with the help of Moran and Geary . Local Moran was used to detect hotspots and cold spots, whereas local Getis Ord G was used to identify only spatial hotspots. The spatial heterogeneity has been detected using various conventional and spatial models, including the Poisson‐Gamma model, the Poisson‐Lognormal Model, the Conditional Autoregressive (CAR) model, the Convolution model, and the BYM2 model, respectively. These models are implemented using Gibbs sampling and other Bayesian hierarchical approaches to analyze the posterior distribution effectively, enabling inference within a Bayesian context.ResultsThe study's findings show that Moran and Geary analysis provides a substantial clustering pattern of positive spatial autocorrelation of dengue fever (DF) rates between surrounding districts at a 90% confidence interval. The Local Indicators of Spatial Autocorrelation cluster mapped spatial clusters and outliers based on prevalence rates, while the local Getis‐Ord G displayed a thorough breakdown of high or low rates, omitting outliers. Although Chattogram had the most dengue cases (15,752), Khulna district had a higher prevalence rate (133.636) than Chattogram (104.796). The BYM2 model, determined to be well‐fitted based on the lowest Deviance Information Criterion value (527.340), explains a significant association between spatial heterogeneity and prevalence rates.ConclusionThis research pinpoints the district with the highest prevalence rate for dengue and the neighboring districts that also have high risk, allowing government agencies and communities to take the necessary precautions to mollify the risk effect of DF.

Probabilistic Prediction of Floating Offshore Wind Turbine Platform Motions via Uncertainty Quantification and Information Integration

The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.

Influence of Childhood Maltreatment on Machiavellianism

ABSTRACT Machiavellianism is characterized by manipulative behavior, lack of empathy, and a willingness to deceive and exploit others for personal gain. Research indicates that one factor that may increase the likelihood and severity of Machiavellianism in adulthood is maltreatment in childhood. However, few studies have attempted to untangle which specific instantiations of childhood maltreatment (e.g. abuse vs. neglect) are most associated with Machiavellianism. In our study, we investigate the association between childhood abuse and neglect on Machiavellianism in a sample of adult women and men recruited online (N = 278) using a Bayesian approach to multiple regression. Results suggest that higher levels of Machiavellianism are associated with physical neglect, with some influence of other forms of maltreatment depending on gender. These findings shed light on potential etiological influences on Machiavellianism and have implications for research on and clinical intervention with people with high levels of this trait.

Liver fibrosis is associated with left ventricular remodeling: insight into the liver-heart axis.

In nonalcoholic fatty liver disease (NAFLD), liver fibrosis is the strongest predictor of adverse outcomes. We sought to investigate the relationship between liver fibrosis and cardiac remodeling in participants from the general population using magnetic resonance imaging (MRI), as well as explore potential mechanistic pathways by analyzing circulating cardiovascular biomarkers. In this cross-sectional study, we prospectively included participants with type 2 diabetes and individually matched controls from the SCAPIS (Swedish CArdioPulmonary bioImage Study) cohort in Linköping, Sweden. Between November 2017 and July 2018, participants underwent MRI at 1.5 Tesla for quantification of liver proton density fat fraction (spectroscopy), liver fibrosis (stiffness from elastography), left ventricular (LV) structure and function, as well as myocardial native T1 mapping. We analyzed 278 circulating cardiovascular biomarkers using a Bayesian statistical approach. In total, 92 participants were enrolled (mean age 59.5 ± 4.6 years, 32 women). The mean liver stiffness was 2.1 ± 0.4 kPa. 53 participants displayed hepatic steatosis. LV concentricity increased across quartiles of liver stiffness. Neither liver fat nor liver stiffness displayed any relationships to myocardial tissue characteristics (native T1). In a regression analysis, liver stiffness was related to increased LV concentricity. This association was independent of diabetes and liver fat (Beta = 0.26, p = 0.0053), but was attenuated (Beta = 0.17, p = 0.077) when also adjusting for circulating levels of interleukin-1 receptor type 2. MRI reveals that liver fibrosis is associated to structural LV remodeling, in terms of increased concentricity, in participants from the general population. This relationship could involve the interleukin-1 signaling. Liver fibrosis may be considered a cardiovascular risk factor in patients without cirrhosis. Further research on the mechanisms that link liver fibrosis to left ventricular concentricity may reveal potential therapeutic targets in patients with non-alcoholic fatty liver disease (NAFLD). Previously, studies on liver fibrosis and cardiac remodeling have focused on advanced stages of liver fibrosis. Liver fibrosis is associated with left ventricular (LV) concentricity and may relate to interleukin-1 receptor type 2. Interleukin-1 signaling is a potential mechanistic interlink between early liver fibrosis and LV remodeling.

REGRESSION OF SURVIVAL DAGUM 3 PARAMETER LINK FUNCTION IN HIV/AIDS PATIENTS IN EAST JAVA

AIDS (Acquired Immuno Deficiency Syndrome) is is a symptom of blood cell disease which results in decreased immunity in humans. HIV/AIDS causes a decline in economic productivity, health, and environmental fields, so it needs treatment to reduce the spread of the disease in East Java. The spread of the HIV/AIDS virus is growing rapidly both in terms of region and pattern of spread. Therefore, it is necessary to have policies from the government to prevent the spread of HIV/AIDS through mathematical modeling to determine the actual and comprehensive factors that influence HIV/AIDS care. policies needed to handle and reduce HIV/AIDS in East Java. This study aims to determine the factors that influence the care of people with HIV/AIDS with Survival Regression modeling Dagum 3 Parameter link function. This model uses parameter estimation with a Bayesian analysis approach with the results that the factors that significantly influence the length of HIV/AIDS care in East Java are marital status (X5), absolute CD4 levels (X7), Suffering Stage (X8), Functional State (X9), and Therapy adherence (X10). These results indicate that marital status and condition characteristics of HIV/AIDS sufferers are important information in the socialization program for reducing HIV/AIDS in East Java.

A novel Bayesian optimal detector-based approach for determining the first arrival time of wire breakage-induced near-wall acoustic wave in PCCPs

A novel Bayesian optimal detector-based approach for determining the first arrival time of wire breakage-induced near-wall acoustic wave in PCCPs

Climate impacts on high-intensity sapling mortality: Comparative study and future warnings for Larix kaempferi, Abies sachalinensis, and Picea glehnii

Climate change profoundly affects plant viability and forest dynamics, creating uncertainty in future forest management. Post-planting, high-intensity sapling mortality may require costly replanting, disrupting plans, and risking forest management goals. Therefore, this study focused on high-intensity sapling mortality (mortality rates exceeding 30 %) in Larix kaempferi, Abies sachalinensis, and Picea glehnii planted in northern Japan attributed to summer drought. Utilizing a dataset spanning approximately three decades, the correlation between high-intensity mortality occurrence and climatic stress was explored employing a Bayesian approach. Additionally, the probability of high-intensity mortality occurrence by the end of this century was predicted based on two representative climate scenarios (RCP2.6 and RCP8.5). The predictive accuracy of high-intensity mortality occurrence probabilities exceeded 70 % after comprehensive consideration of climatic stress factors, thereby accentuating the robustness of the proposed model. These encompass the antecedent precipitation index (API), which serves as a soil drought indicator; 30-day mean daily maximum temperature (MaxTMP̅); 30-day mean daily total solar radiation (TSR̅); and day of year (DOY). All sapling species manifested a pronounced negative correlation between API and the probability of high-intensity mortality occurrence, whereas TSR̅ exhibited a positive impact on this probability. Additionally, MaxTMP̅ positively influenced this probability for L. kaempferi and A. sachalinensis. Delving into interspecific differentials, API exhibited the most pronounced impact on P. glehnii in comparison to the other two tree species, whereas TSR̅ exerted a substantial influence on A. sachalinensis and P. glehnii. The susceptibility to MaxTMP̅ was hierarchically ordered as L. kaempferi >A. sachalinensis >P. glehnii. DOY, used to assess phenological effects like leaf morphology, negatively affected the probability of high-intensity mortality occurrence exclusively for L. kaempferi. The median probability of high-intensity mortality occurrence in Hokkaido, calculated from the estimated probabilities across all regions of Hokkaido (encompassing non-forested areas but excluding remote islands), at a 1 km resolution, was 0.37 for L. kaempferi, 0.57 for A. sachalinensis, and 0.41 for P. glehnii under RCP2.6 by the century’s end. These probabilities have decreased in comparison to those observed in 2018–2022. In stark contrast, under RCP8.5, the probabilities soared to 0.91, 0.87, and 0.56, representing an increase of 2.12 times for L. kaempferi, 1.36 times for A. sachalinensis, and 1.14 times for P. glehnii in comparison to 2018–2022. Even the least affected species, P. glehnii, witnessed a increase in mortality probability. This study underscores the importance of forest management and adaptation to climate change.

Estimation of ventricular ectopic beat origin with electrocardiographic imaging

Abstract Background Precise identification of premature ventricular contractions (PVC) origins poses a significant challenge in electroanatomic mapping. Electrocardiographic Imaging (ECGI) allows instantaneous and global activation time mapping, providing a fast way to locate the origin of ectopic contractions. However, the standard methodology to estimate local activation times (LAT) in invasive electrograms is not directly transferable to ECGI signals. Objective In this study, a new methodology to calculate LAT maps with ECGI is presented and compared to the standard method in both, mathematical simulations and patients. Method We conducted computer simulations of five distinct PVCs to generate ECGI signals. Two techniques were applied to estimate LATs: the conventional -dV/dt method, which identifies the steepest negative deflection, and our novel Bayesian approach. The error in the location of the origin of the electrical activation and the similarity of the LAT maps were calculated with respect to the reference computer model. Additionally, we tested both algorithms in four patients with PVCs originated in different locations (three in the output tract of the right ventricle and one in the ventricular base, close the coronary sinus). Results The results indicated a substantial improvement in identifying the earliest activation site using our Bayesian algorithm, with a reduced localization error (1.5 ± 2.5 cm to 0.6 ± 1.5 cm). The root mean squared error (RMSE) was also significantly lower when comparing our Bayesian LAT maps to the reference LATs obtained in the simulations (19.0 ± 5.3 vs. 10.8 ± 4.5 ms). Patient data revealed smoother and more physiologically consistent propagations in the Bayesian maps, aligning with the PVC origins identified via electroanatomical navigation systems. Conclusion This study demonstrates that ECGI signals, when processed with advanced Bayesian techniques, significantly enhance LAT estimation. The proposed methodology improves the localization accuracy of ectopic beats and has practical implications for optimizing PVC treatment protocols.

Daily social comparisons among women in midlife with elevated risk for cardiovascular disease: A within-person test of the identification/contrast model.

Women age 40-60 are disproportionately affected by health problems that increase their risk for cardiovascular disease (CVD; e.g. hypertension). Social comparisons (i.e. self-evaluations relative to others) are known to influence health in this and other groups, but their nature and consequences in daily life are poorly understood. We conducted an ecological momentary assessment study over 10days (5x/day) with 75 women ages 40-60 who had ≥1 CVD risk conditions (MAge = 51.6years, MBMI = 34.0kg/m2). Using a mix of frequentist and Bayesian analytic approaches, we examined characteristics of women's naturally occurring comparisons and tested predictions from the Identification/Contrast Model within-person (e.g. identifying with an upward target results in positive affect, whereas contrasting results in negative affect). Comparisons occurred at 21% of moments, with considerable within-person variability in response. In line with predictions from the Identification/Contrast Model, women were more likely to experience positive affect after upward identification or downward contrast and more likely to experience negative affect after upward contrast or downward identification, though observed nuances warrant additional consideration. Overall, findings support the Identification/Contrast Model to describe women's comparison experiences as they occur in daily life. Future work should determine pathways between the immediate consequences of comparisons and longer-term health outcomes.

Short-term traffic flow prediction via weight optimization of composite models

ABSTRACT Accurate prediction of peak-hour traffic flow is crucial for congestion management. Neural network models have varying performance in different domains, limiting their effectiveness in traffic data. To address this, we propose the SSA-BiLSTM-BP-Elman ensemble model. It integrates bi-directional long short-term memory (BiLSTM), back propagation (BP) neural network, and Elman network. The model uses the sparrow search algorithm (SSA) to optimize the weight distribution to improve the prediction accuracy. Initially, the BiLSTM, BP, and Elman models are parameter-optimized by the Bayesian approach. SSA then assigns unique weights to each model based on the characteristics of the extracted data features. Results from an application to UK high-speed traffic flow data show that SSA significantly improves the accuracy of model predictions. This integrated model effectively utilizes the strengths of each model by assigning appropriate weighting coefficients by SSA, thus improving the overall prediction performance.

Bayesian effect selection in structured additive quantile regression

Bayesian structured additive quantile regression is an established tool for regressing outcomes with unknown distributions on a set of explanatory variables and/or when interest lies with effects on the more extreme values of the outcome. Even though variable selection for quantile regression exists, its scope is limited. We propose the use of the Normal Beta Prime Spike and Slab (NBPSS) prior in Bayesian quantile regression to aid the researcher in not only variable but also effect selection. We compare the Bayesian NBPSS approach to statistical boosting for quantile regression, a current standard in automated variable selection in quantile regression, in a simulation study with varying degrees of model complexity and illustrate both methods on an example of childhood malnutrition in Nigeria. The NBPSS prior shows good performance in variable and effect selection as well as prediction compared to boosting and can thus be recommended as an additional tool for quantile regression model building.