Bayesian Estimation Research Articles

Impact of carbon fiscal policy on Zhejiang Economy—Evidence from E-DSGE

This paper studies the impact of carbon fiscal policy on the Zhejiang economy through the lens of a DSGE model with an environmental sector. The model was estimated via Bayesian estimation using data from Zhejiang Province for the period from 2005Q1 to 2021Q4. We found that both carbon tax and carbon emission subsidy can improve environmental quality and reduce carbon emissions. However, the subsidy tends to stimulate output and employment more, while both policies have a negative impact on consumption and investment. We suggest that the government should exercise caution in implementing these policies, as their scale of impact is relatively small. The combination of the two policies could neutralize their impact on output but may enhance their impact on other sectors in Zhejiang. However, the structure and timing of these policies matter; implementing a low carbon tax first followed by a high carbon subsidy would be preferable. Furthermore, we examined and concluded that emission technology, particularly at the firm level, can play a significant role in mitigating the negative impact.

Reliability estimation in multicomponent stress-strength model using weighted exponential-Lindley distribution

This research investigates the reliability estimation in multicomponent stress-strength model when both the stress and strengths are drawn from Weighted Exponential-Lindley distribution. Reliability assessment is carried out using classical and Bayesian approaches. The Bayes estimates of the reliability in the multicomponent stress-strength model are derived under a squared error loss function using informative and non-informative priors for the parameters. Further, Lindley's approximation and Gibbs sampling method are used to develop Bayes estimators for the system's reliability due to the lack of explicit forms. Additionally, an asymptotic confidence interval and the highest probability density credible interval are constructed to gauge system performance. A simulation study is conducted to assess the performance of reliability estimators. Finally, a real data set is analysed for illustrative purposes.

Testing extinction events and temporal shifts in diversification and fossilization rates through the skyline Fossilized Birth-Death (FBD) model: The example of some mid-Permian synapsid extinctions.

In the last decade, the Fossilized Birth-Death (FBD) process has yielded interesting clues about the evolution of biodiversity through time. To facilitate such studies, we extend our method to compute the probability density of phylogenetic trees of extant and extinct taxa in which the only temporal information is provided by the fossil ages (i.e. without the divergence times) in order to deal with the piecewise constant FBD process, known as the "skyline FBD", which allows rates to change between pre-defined time intervals, as well as modelling extinction events at the bounds of these intervals. We develop approaches based on this method to assess hypotheses about the diversification process and to answer questions such as "Does a mass extinction occur at this time?" or "Is there a change in the fossilization rate between two given periods?". Our software can also yield Bayesian and maximum-likelihood estimates of the parameters of the skyline FBD model under various constraints. These approaches are applied to a simulated dataset in order to test their ability to answer the questions above. Finally, we study an updated dataset of Permo-Carboniferous synapsids to get additional insights into the dynamics of biodiversity change in three clades (Ophiacodontidae, Edaphosauridae and Sphenacodontidae) in the Pennsylvanian (Late Carboniferous) and Cisuralian (Early Permian), and to assess support for end-Sakmarian (or Artinskian) and end-Cisuralian mass extinction events discussed in previous studies.

Reliability estimation and order-restricted inference based on joint type-II progressive censoring scheme with application to splashing data in atomization process

Censoring commonly occurs in real-world scenarios, either intentionally or unintentionally. In addition, in engineering and applied studies, two or more populations are required to perform the experiment. In this article, we develop the order-restricted ( OR) statistical inference of two inverted exponentiated Rayleigh ( IERayleigh) populations based on the joint type-II progressive censoring scheme ( J-IIProgCS). We also consider the problem of the stress-strength reliability Re = P( X> Y) estimation under J-IIProgCS. The process of estimating the parameters with order restriction ( PaR-OR) and Re are performed using the maximum likelihood (ML) and Bayesian methods. On the presumption that the gamma priors are independent, the Bayesian estimate of reliability model is obtained using the hybrid Gibbs within Metropolis-Hastings ( G-MeHa) algorithm. Moreover, with assumption the ordered Beta-Gamma prior for the shape parameters and the Gamma prior for the common scale parameter, the Bayes estimates of the parameters with order restriction are evaluated. The classical confidence intervals and the Bayesian credible interval are created. A detailed numerical illustration using simulated data sets is carried out. A real engineering example related to splashing phenomenon is considered for illustrative purposes.

External financial and monetary policy shocks: Do they matter for Korean macroeconomy?

Given Korea's status as a small, open economy, it exhibits a pronounced sensitivity to external shocks. Consequently, this article seeks to elucidate the impact of external financial and monetary policy shocks on the fluctuation of critical macroeconomic variables within Korea. Employing Bayesian estimation alongside the impulse response function for empirical analysis, the findings reveal that external financial and monetary policy shocks precipitate declines in exports, output, employment, real wages, consumption, investment, and imports. Conversely, these shocks are associated with increases in both the price level and inflation, highlighting the multifaceted effects of external pressures on the domestic economic landscape. Further, through forecast error variance decomposition, this study demonstrates that, relative to shocks stemming from productivity, terms of trade, and real exchange rate variations, external financial and monetary policy shocks exert a considerably milder impact on the fluctuations of Korea's key macroeconomic variables. This insight suggests a potential area for enhancement in the existing Korean literature on this topic, advocating for the integration of these findings to enrich understanding and analysis. In summary, by delving into the nuanced effects of external shocks on Korea's economy, this article contributes valuable perspectives to the discourse, suggesting avenues for further research and policy formulation. The integration of these results into the broader body of Korean economic literature could significantly augment current understandings and interpretations of Korea's economic dynamics in the face of global financial and monetary turbulence.

Problem orientation as a moderator of the relation between discrimination and passive suicidal ideation

BackgroundPassive suicidal ideation (i.e., the desire to be dead) may be an important, yet understudied, risk factor for suicidal behavior. Experiences of discrimination have been linked to greater severity of suicidal ideation though less work has examined factors that influence this relation. Though not explicitly tested, prior work suggests that problem orientation, defined as an individual's general beliefs about problems in daily life, may be an important moderator of this relation. In the present study, positive and negative problem orientation were hypothesized to moderate the relation between everyday discrimination and past month frequency of passive suicidal ideation. MethodThe study consisted of a diverse (62.4 % non-White; 21.4 % Latino/a/x; 27 % sexual minority) adult sample (N = 392; Mage = 20.24, SD = 2.49, 67.3 % female) recruited from a U.S. Mid-Atlantic college campus. Discrimination, problem orientation, past-month depressive symptoms, and past-month frequency of passive suicidal ideation were assessed using self-report measures. A single path model with Bayesian estimation was used to evaluate concurrent relations among study variables. ResultsAfter controlling for depressive symptoms, sex, sexual identity, and age, positive problem orientation buffered the impact of lifetime discrimination on past month passive suicidal ideation (b = -0.01, 95 % credibility interval [CI]: [-0.008, -0.001]). Negative problem orientation did not moderate this relation (b = 0.00, 95 % CI: [-0.002, 0.003]). LimitationsStudy design was cross-sectional in nature. ConclusionsResults suggest that bolstering positive problem orientation in treatment with young adults who have experienced discrimination may help reduce suicide risk.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Prevalence of opioid dependence in Scotland 2015-2020: A multi-parameter estimation of prevalence (MPEP) study.

Drug-related deaths in Scotland more than doubled between 2011 and 2020. To inform policymakers and understand drivers of this increase, we estimated the number of people with opioid dependence aged 15-64 from 2014/15 to 2019/20. We fitted a Bayesian multi-parameter estimation of prevalence (MPEP) model, using adverse event rates to estimate prevalence of opioid dependence jointly from Opioid Agonist Therapy (OAT), opioid-related mortality and hospital admissions data. Estimates are stratified by age group, sex and year. Scotland, 2014/15 to 2019/20. People with opioid dependence and potential to benefit from OAT, whether ever treated or not. Using data from the Scottish Public Health Drug Linkage Programme, we identified a baseline cohort of individuals who had received OAT within the last 5years, and all opioid-related deaths and hospital admissions (whether among or outside of this cohort). Rates of each adverse event type and (unobserved) prevalence were jointly modelled. The estimated number and prevalence of people with opioid dependence in Scotland in 2019/20 was 47 100 (95% Credible Interval [CrI] 45 700 to 48 600) and 1.32% (95% CrI 1.28% to 1.37%). Of these, 61% received OAT during 2019/20. Prevalence in Greater Glasgow and Clyde was estimated as 1.77% (95% CrI 1.69% to 1.85%). There was weak evidence that overall prevalence fell slightly from 2014/15 (change -0.07%, 95% CrI -0.14% to 0.00%). The population of people with opioid dependence is ageing, with the estimated number of people aged 15-34 reducing by 5100 (95% CrI 3800 to 6400) and number aged 50-64 increasing by 2800 (95% CrI 2100 to 3500) between 2014/15 and 2019/20. The prevalence of opioid dependence in Scotland remained high but was relatively stable, with only weak evidence of a small reduction, between 2014/15 and 2019/20. Increased numbers of opioid-related deaths can be attributed to increased risk among people with opioid dependence, rather than increasing prevalence.

Reliability Estimation for Burr XII Distribution under the Weighted Q-Symmetric Entropy Loss Function

Considering that the choice of loss function plays a significant role in the derivation of Bayesian estimators, we propose a novel asymmetric loss function named the weighted Q-symmetric entropy loss for computing the estimates of the parameter and reliability function of the Burr XII distribution. This paper covers the classical maximum-likelihood, uniformly minimum-variance unbiased, and Bayesian estimation methods under the squared error loss, general entropy loss, Q-symmetric entropy loss, and new loss functions. Through Monte Carlo simulation, the respective performances of the considered estimators for the reliability function are evaluated, indicating that the Bayesian estimator under the new loss function is more efficient than those under other loss functions. Finally, a real data set is used to demonstrate the practicality of the presented estimators.

Bayesian inference for the inverse Weibull distribution based on symmetric and asymmetric balanced loss functions with application

In this study, the unified hybrid censored approach is employed to estimate the parameters of the inverse Weibull distribution, as well as the survival and hazard rate functions. Parameter estimates are obtained using both Bayesian and Maximum Likelihood approaches, with Bayesian estimates acquired through Lindley's approximation method using three distinct balanced loss functions. These encompass both symmetric and asymmetric balanced loss functions, specifically the balanced squared error (BSE) loss function, the balanced linear exponential (BLINEX) loss function, and the balanced general entropy (BGE) loss function. We conduct a simulation study to compare the effectiveness of various estimators, and a real-world data analysis is presented to illustrate practical implementation. Ultimately, our findings indicate that Bayesian parameter estimates consistently outperform their Maximum Likelihood counterparts across all methods.

Social Networks and Communication Impairment: Racial Differences Among Stroke Survivors

Purpose: Recent evidence highlights the importance of social networks—the composition of social relationships—and their characteristics in stroke recovery. This study explores the relationship between social networks and poststroke communication outcomes. Method: A cohort of 30 non-Hispanic Black ( n = 12) and non-Hispanic White ( n = 18) adult stroke survivors completed the Stroke Impact Scale (SIS) Communication subtest and the Stroke Social Network Scale (SSNS). The SSNS captures elements of social networks including satisfaction and contact with friends, relatives, children, and groups. The relationship between the SIS Communication subtest scores and SSNS scores was explored using ordinal Bayesian regression estimation models adjusting for sex, age, time postonset, marital status, and stroke type. Results: Average SIS Communication scores differed ( t = 2.07, p = .335) between White ( M = 81.75, SD = 22.90) and Black ( M = 95.71, SD = 4.95) participants, but SSNS Comprehensive scores were similar between the two groups ( t = −0.89, p = 1.00). SSNS children subtest scores were higher among the Black than the White ( t = −1.73, p = .0473) participants. Similarly, satisfaction subtest scores were comparatively higher among Black than White participants ( t = 1.85, p = .451). SSNS Satisfaction, Friends, and Groups subtest scores were positively associated with SIS Communication, indicating strong associations with lower impairment. SSNS Children subtest scores were negatively related to SIS, indicating higher impairment. Although most subtest associations were similar for both groups, the associations with SSNS Children and Friends subtests differed between racial groups. Conclusions: Levels of communication impairment were correlated with child, friendship, and group attachments among participants. However, strong social ties to children and friends had different associations among racial groups.

Multidimensional credibility: A new approach based on joint distribution function

Abstract In the traditional multidimensional credibility models developed by Jewell ((1973) Operations Research Center, pp. 73–77.), the estimation of the hypothetical mean vector involves complex matrix manipulations, which can be challenging to implement in practice. Additionally, the estimation of hyperparameters becomes even more difficult in high-dimensional risk variable scenarios. To address these issues, this paper proposes a new multidimensional credibility model based on the conditional joint distribution function for predicting future premiums. First, we develop an estimator of the joint distribution function of a vector of claims using linear combinations of indicator functions based on past observations. By minimizing the integral of the expected quadratic distance function between the proposed estimator and the true joint distribution function, we obtain the optimal linear Bayesian estimator of the joint distribution function. Using the plug-in method, we obtain an explicit formula for the multidimensional credibility estimator of the hypothetical mean vector. In contrast to the traditional multidimensional credibility approach, our newly proposed estimator does not involve a matrix as the credibility factor, but rather a scalar. This scalar is composed of both population information and sample information, and it still maintains the essential property of increasingness with respect to the sample size. Furthermore, the new estimator based on the joint distribution function can be naturally extended and applied to estimate the process covariance matrix and risk premiums under various premium principles. We further illustrate the performance of the new estimator by comparing it with the traditional multidimensional credibility model using bivariate exponential-gamma and multivariate normal distributions. Finally, we present two real examples to demonstrate the findings of our study.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

Dryland Performance Tests Are Not Good Predictors of World Aquatics Points in Elite Male and Female Swimmers.

Background: Swim performance can be reliant on strength and power. Standardisation of swim performance in different events, distances, and sexes can be completed using World Aquatics points, allowing for ranking of swimmers. The aim of this retrospective cross-sectional study was to assess whether relationships between World Aquatics points and dryland markers of performance existed in male and female elite swimmers separately and combined. Methods: Dryland tests included Optojump® photoelectric cell countermovement jump, countermovement jump reach with a Vertec® system, standing broad jump using a tape measure, repetition maximum testing in the barbell back squat, barbell deadlift, and barbell bench press. Swim performance data and dryland test data on elite male (n = 38) and female (n = 20) Scottish swimmers from 2009-2017 were collected. Swim performance data were converted to World Aquatics federation points, and Bayesian linear regression analyses examined relationships between World Aquatics points and dryland performance tests: countermovement jump height (cm) using an Optojump® photoelectric cells system, countermovement jump height (cm) using a Vertec® device, standing broad jump distance (cm), relative strength (load lifted (kg) per kg of body mass) in the barbell bench press (kg/kg), barbell back squat (kg/kg), barbell deadlift (kg/kg). Results: The Bayesian estimates of change of World Aquatics points for a unit change in jump-based measures were: Optojump®-men = 0.6, women = 0.6, combined = 0.4; Vertec®-men = 4.3, women = -1.6, combined = 2.4; standing broad jump-men = 0, women = 0, combined = 0.4. Strength-based measures were: barbell back squat-men = 2.3, women = 22, combined = -2.5; barbell deadlift-men = -5; barbell bench press-men = 41.8. Conclusions: Dryland performance tests are not good predictors of World Aquatics points and should rather be used for assessing training quality and monitoring injury risks.

Handling Measurement Error and Omitted Confounders Considering Informativeness of the Confounding Effect under Mediation Modeling

In the article, we focused on the issues of measurement error and omitted confounders while conducting mediation analysis under experimental studies. Depending on informativeness of the confounders between the mediator (M) and outcome (Y), we described two approaches. When researchers are confident that primary confounders are included (e.g., based on theory, literature), mediation effects can be estimated using Bayesian estimation with informative priors for the correlation of residuals between latent M and Y (ρ). Simulation study results showed that mediation analysis without accounting for secondary confounders yielded inaccurate inferences about mediation effects. Without prior knowledge about the primary confounders between M and Y, we suggest using sensitivity analysis to probe robustness of mediation analysis results. ρ was used as the sensitivity parameter and an R shiny app was developed to conduct sensitivity analysis: https://qwang17.shinyapps.io/sensitivity/. Both unidimensional and multidimensional models are allowed under our developed R shiny app to conduct sensitivity analysis. Two real data examples were used for illustrating the procedure of sensitivity analysis. We ended the article by making recommendations and entertaining future directions in mediation analysis.

The 3-component mixture of power distributions under Bayesian paradigm with application of life span of fatigue fracture

Mixture distributions are naturally extra attractive to model the heterogeneous environment of processes in reliability analysis than simple probability models. This focus of the study is to develop and Bayesian inference on the 3-component mixture of power distributions. Under symmetric and asymmetric loss functions, the Bayes estimators and posterior risk using priors are derived. The presentation of Bayes estimators for various sample sizes and test termination time (a fact of time after that test is terminated) is examined in this article. To assess the performance of Bayes estimators in terms of posterior risks, a Monte Carlo simulation along with real data study is presented.

The optimal multi-stress–strength reliability technique for the progressive first failure in the length-bias exponential model using Bayesian and non-Bayesian methods

In many real-world situations, systems frequently fail in their demanding operational settings. Researchers pay little attention to the fact that systems typically fail to execute their intended activities when it reaches its extreme operating situations as appropriate. In this paper, we try to develop and study inferential aspect of a system reliability having multiple components based on a progressive first failure censoring scheme assuming unit length-bias exponential distribution. Regarding estimation, asymptotic, boot-p, and boot-t approaches under the interval estimation are adopted, while the maximum likelihood method under the point estimation is considered. The MCMC method is used to get the Bayes estimate of the reliability parameter assuming both the symmetric and asymmetric loss functions. The associated confidence intervals are also reported as appropriate. The effectiveness of the various adopted estimation strategies is evaluated and compared using Monte Carlo simulation studies and examples from real-world applications.

The relationships between supervisor and colleague support interaction with teacher presenteeism and work engagement: a multilevel moderated mediated analysis

AbstractIn this study, a multilevel moderated mediation model is analyzed using the motivational pathway of the job demands-resources (JD-R) model to explore the connections that can prevent teacher presenteeism. We utilized a multilevel structural equation modelling (MSEM) approach with Bayesian estimation to examine the relationships between teacher presenteeism, work engagement, supervisor support, and colleague support. We used data collected from a sample of teachers working in public schools located in the city center of Elâzığ, which is situated in the eastern part of Turkey. The results indicated that there is a positive relationship between supervisor support and work engagement, as well as a negative relationship between supervisor support and teacher presenteeism through work engagement. Furthermore, we found that under conditions of colleague support, these relationships are strengthened, indicating that colleague support plays a moderating role in these relationships. Finally, following the presentation of the theoretical contributions of the findings, we provide educational implications that would help isolate teachers from the risk of presenteeism and foster positive work emotions.

ALLEVIATING SURVEYOR BIAS IN REAL ESTATE: AN APPLICATION TO VACANCY AND PROPERTY PRICES

Although vacancies in the real estate market have always been of key interest to both private and public stakeholders, measuring the impact of vacancy rates on property prices is challenging. This study attempts to quantify the extent of the influence of vacancy rates on the price decline of commercial properties. The dataset used in this study was collected and collated by valuation experts. We attempt to alleviate the bias inherent in the price estimates provided by the surveyors. A Bayesian multilevel estimation model was employed, and the results revealed that while some experts do not show deviations from the standard tendency of a peer group, others do diverge from this norm. Based on these findings, a bias-controlling price decline rate was derived. As many real estate statistics are produced based on survey data compiled by experts, the approach adopted in this study is expected to be applied to various real estate practices to mitigate the inherent and inevitable surveyor bias.

Statistical inference of comparative generalized inverted exponential populations under joint adaptive progressive type-II censored samples

Design of efficient test plans to assessing product reliability rapidly, is a critical step to obtain product reliability accurately. In this paper, we are adopted the problem of statistical inference of two generalized inverted exponential populations in a competing duration. To save the balance between the optimal test time and the number of failures needing for statistical inference, joint adaptive type-II hybrid progressive censoring scheme is applied. The model parameters are estimated with classical methods (maximum likelihood and bootstrap) for point estimate and the corresponding confidence intervals. Also, Bayes estimators of model parameters are computed under importance sample technique with the corresponding Bayes credible intervals. A real data sets are analyzed for illustrating purposes. The results are compared and assessed through Monte Carlo simulation study.