Simulating a Two-Arm Random Allocation Model Using a Bayesian Dynamic Linear Model Approach

Fisheries bycatch threatens the viability of some seabird populations and reduces fishing efficiency. Albatross bycatch in a US North Pacific tuna longline fishery has increased over the past decade and now exceeds 1000 annual captures. Seabirds interacting with this fishery reach hooks at depths up to 1 m. A branchline weight’s mass and distance from the hook affect seabird catch rates. We conducted experimental fishing to compare the commercial viability of a weighted hook relative to conventional gear with weights attached 0.75 m from the hook. We used a Bayesian random effects meta-analytic regression modelling approach to estimate pooled expected species-specific log relative risk of capture on conventional versus experimental gear. There was a significant 53% (95% HDI: − 75 to − 25%) decrease in retained species’ catch rates on experimental hooks, indicating an unacceptable economic cost, and no significant effect for discarded species. Using a Bayesian general linear mixed regression modelling approach, experimental hooks sank to 85 cm ca. 1.4 times (95% HDI: 1.37–1.48) faster than control hooks. Given their potential to reduce seabird catch rates, eliminate safety risks from bite-offs and facilitate robust compliance monitoring, it is a priority to find a weighted hook design with acceptable catch rates.

설문조사를 실시할 때 응답자가 설문조사의 일부 문항에 대하여 응답하지 않는 경우 항목무응답이 발생한다. 무응답이 발생한 자료를 제외하고 완전하게 응답된 자료 만에 근거한 분석은 분석 결과에 편의가 발생할 수 있으므로, 이를 채워 넣어 완전한 형태의 자료로 분석하기 위해서 무응답 대체가 흔히 사용되고 있으며 여러 가지 무응답 대체 기법들을 비교하는 연구들도 많이 존재한다. 패널조사 연구는 연구 대상 패널에 대하여 정해진 시간에 따라 반복적으로 동일한 설문 문항에 대하여 응답을 조사하여 시간에 따른 변화를 살펴보는 조사 방법을 나타낸다. 패널조사 자료의 항목 무응답을 대체할 때 이전 시점의 응답 자료가 존재한다면 이를 포함하여 대체를 실시하는 것이 바람직한 것으로 여겨져 왔으나 이에 관한 직접적인 연구는 찾기 힘들다. 따라서 본 연구에서는 패널자료에서 이전 시점의 정보를 고려하지 않고 대체를 실시하는 방법과 이전 시점의 정보를 활용하여 대체하는 방법들 중에서 어느 대체 방법이 보다 적절한 대체를 제공하는지 살펴보았다. 특히 이전 시점의 응답 정보를 이용하는 방법인 비대체, 선형혼합모형을 이용한 대체와 선형혼합모형에 근거한 베이지안 대체 방법을 고려하였고, 이를 이전 시점의 정보를 고려하지 않는 대체 방법들 중 흔히 사용되는 평균대체, 핫덱대체 방법과 비교하였다. 모의실험 결과 선형혼합모형에 근거한 베이지 안 대체 방법이 다른 대체 방법에 비해 무응답 비율이 높아지더라도 편의도 작으며 평균에 관한 95% 신뢰구간의 포함률도 높게 나타나서 가장 좋은 대체 방법으로 확인되었다. When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

Seasonal prediction of western North Pacific summer monsoon rainfall (WNPSMR) is in great demand but remains challenging, because the relationships between the Asian monsoon system and precursors are nonstationary and exhibit significant decadal changes. The present study aims to (1) examine decadal variations of the relationships between the WNPSMR and predictors used in previous studies and (2) establish a new prediction model using a Bayesian dynamical linear model (DLM), which is capable of capturing the time-evolving relationships between the predictand and predictors whereas the conventional static linear model cannot. Two predictors were selected previously to predict the WNPSMR. One is the sea level pressure tendency anomalies over the tropical eastern Pacific from late spring to early summer, which represents remote forcing related to ENSO and has a stable effect on WNPSMR throughout the analysis period. The other is the sea surface temperature anomaly difference between the northern Indian Ocean (IO) and the WNP during spring through early summer (called IOWPSST), which denotes local air–sea interaction that affects the WNP subtropical high. Results show that the IOWPSST has strong influence on WNPSMR during 1979–2003 (period 1), while from 2004 to 2017 (period 2) its connection to WNPSMR evidently weakens. This nonstationary relationship is due to the non-persistence of the enhanced WNP subtropical high during period 2, which is associated with the positive-to-negative phase transition of the Interdecadal Pacific Oscillation since ~ 2000. A new prediction model was established using the two predictors with Bayesian DLM. The cross-validation method and a 9-years independent forward-rolling forecast is applied to test the hindcast and actual forecast ability. Results show that the Bayesian DLM has higher hindcast/forecast skill and lower mean square error compared with static linear model, suggesting that the DLM has advantage in predicting WNPSMR and is a promising method for seasonal prediction.

Aim/Purpose: This study aimed to increase our understanding of how the stages of the customer purchase journey, privacy trade-offs, and information sensitivity of different business service sectors affect consumers’ privacy concerns. Background: The study investigated young consumers’ willingness to provide consent to use their personal data at different phases of the customer journey. This study also examined their readiness to provide consent if they receive personal benefits, and how information sensitivity varied between different individuals and business sectors. Methodology: Data was collected by a quantitative survey (n=309) and analyzed with R using the Bayesian linear mixed effect modeling approach. The sample consisted of university students in Finland, who represented a group of young and digitally native consumers. The questionnaire was designed for this study and included constructs with primarily Likert-scale items. Contribution: The study contributed to data privacy and consent management research in information sensitivity, privacy trade-off, and the customer journey. The study underlined the need for a stronger user experience focus and contextuality. Findings: The results showed that readiness to disclose personal data varied at different phases of the customer journey as privacy concerns did not decrease in a linear fashion throughout the purchase process. Perceived benefits affected the willingness to provide consent for data usage, but concerned consumers would be less trade-off oriented. Self-benefit was the most relevant reason for sharing, while customization was the least. There is a connection between the information sensitivity of different business sector information and privacy concerns. No support for gender differences was found, but age affected benefits and business sector variables. Recommendations for Practitioners: The study recommends approaching consumers’ data privacy concerns from a customer journey perspective while trying to motivate consumers to share their personal data with relevant perceived benefits. The self-benefit was the most relevant benefit for willingness to provide consent, while customization was the least. Recommendation for Researchers: The study shows that individual preference for privacy was a major factor directly and via interaction for all three models. This study also showed that consumers’ subjective decision-making in privacy issues is both a situational and a contextual factor. Impact on Society: This study could encourage policymakers and societies to develop guidelines on how to develop privacy practices and consent management to be more user centric as individuals are increasingly concerned about their online privacy. Future Research: This study encourages examining consumers’ motivational factors to provide digital consent for companies with experimental research settings. This study also calls to explore perceived benefits in all age groups from the perspective of different information in various business sectors. This study shows that privacy concern is a contextual and situational factor.

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed.

Degradation modeling and remaining useful life (RUL) prediction for products with multiple degradation features are hot topics in the prognostic and health management. The key to this problem is to describe the dependence among multiple degradation features effectively. In this paper, a multivariate degradation modeling approach based on the Bayesian dynamic linear model (BDLM) is proposed to calculate the RULs of degradation features, and the Copula function is employed to capture the dependence among RUL distributions. A combined BDLM is used to establish the multivariate degradation model, which includes two typical BDLMs, namely, the linear growth model and seasonal factors model. After the model parameters get calibrated by the maximum likelihood estimation, the model can predict the degradation process of features. Once the failure thresholds are given, the probability density function and cumulative distribution function (CDF) of RUL for each degradation feature can be obtained. Since these RUL distributions are not independent of each other, the Copula function is adopted herein to couple the CDFs. Finally, some practical testing data of a microwave component, which has two degradation features, are utilized to validate our proposed method. This paper provides a new idea for the multivariate degradation modeling and RUL prediction.

Traditional longitudinal models of adolescent alcohol use often assume that within-person variability-the extent to which an individual's alcohol use fluctuates over time-is the same for everyone. However, in the real world, some adolescents show relatively stable patterns of use, while others fluctuate substantially across different measurement waves. To capture these individual differences, the present study applies a Bayesian hierarchical linear modeling approach that allows between-person differences in within-person variability. This study compared 16 model variants that crossed the following features: within-person error variance (fixed or random), lag effect (present or absent), linear and quadratic effects (random linear and quadratic terms included or excluded), and transformation (probit-transformed or untransformed). The best-fitting model (DIC = -2,615; Deviance = -4,362) included a lag parameter, varying error terms, and random linear and quadratic effects for probit-transformed data. Notably, the average of the within-person standard deviation (${\mu}_{\sigma_j}$ = 0.231) was almost twice that of the varying intercept (${\sigma}_{\beta_{0j}}$ = 0.124), indicating substantial within-person variability in adolescent alcohol use across time. Accounting for heterogeneous within-person variability and modeling alcohol use with a non-normal distribution significantly improved model-data fit and yielded extra insights into adolescents' alcohol use research. This approach allows researchers and practitioners to more accurately identify individuals with irregular or unstable drinking patterns, enhancing early detection and targeted intervention strategies.

Bridge monitoring systems produce a large amount of data, including uniform and non-uniform sampled data in the long-term service periods; the proper handling of these data is one of the main difficulties in structural health monitoring. To properly predict structural non-uniform extreme stress and deal with the uncertainties of the monitored data, the objectives of this article are to present (a) Bayesian dynamic linear models about non-uniform extreme stress, (b) monitoring mechanism about the Bayesian dynamic linear models based on single and cumulative Bayes’ factors, and (c) an effective use of the Bayesian dynamic linear models to incorporate the dynamic monitored data into structural non-uniform extreme stress prediction. The proposed models and procedure are applied to the monitored data obtained from the I-39 Northbound Bridge over the Wisconsin River in Wausau, Wisconsin, USA.

The step test method is a common modeling approach for industrial process and has the disadvantages of time consuming and production disruption. One of the major features of the Bayesian methodology is the ability of integrating the subjective intervention information into existing Bayesian dynamic linear model (DLM) and sometimes the subjective information is also important for a MPC controller design. This paper takes the bottom temperature control problem of an aniline stripping column as the research object, and the model calculated by the Bayesian DLM and linear fitness is adopted to design a MPC controller. Finally, the practical application result shows that the model based on the Bayesian DLM is useful and effective, and by using this modeling method, the disruptive and expensive step test may be reduced.

In this paper we propose a Bayesian approach to statistical analysis of the economy with time-varying total factor productivity (TFP) showing rapid changes. Conventional approaches to the empirical study of economic growth lack flexibility and therefore cannot appropriately capture the complex movement of TFP. To solve this problem, we construct Bayesian models for a production function with dynamic structure. Also, the possibility is considered that there are rapid changes in TFP in some situations. In our proposed approach, TFP is treated as a time-varying parameter and regarded as a random variable. A set of Bayesian models for time-varying TFP incorporating rapid changes is constructed based on a smoothness prior approach. Then, the time-varying TFP is estimated using a Bayesian linear modeling approach together with a newly-proposed random grouping method. Using time series data for the Chinese macro-economy, we show that our proposed approach makes a detailed analysis of TFP trends possible.

Given a population at a specific time point, it is often of interest to identify the entry age into typical stages of life, such as being young, becoming adult and elderly. These age cutoffs are important because they influence the public opinion and have an impact on policy decisions. An issue of great social relevance is defining the threshold beyond which a person becomes elderly. Fixed cutoffs are debatable because of their conventional nature which disregards issues such as changing life expectancy and the evolving structure of the age distribution. The above shortcomings can be overcome if age cutoffs are defined endogenously, i.e., relative to the whole age distribution of each country at a specific time point. We pursue this line of research by presenting an analysis whose main features are: (1) establishing a relationship between a country's welfare regime and its age distribution and aging process, together with the identification of four clusters of countries corresponding to distinctive welfare models and (2) a Bayesian hierarchical dynamic model which accounts for the uncertainty in the time series of measurements of the endogenous cutoffs for the countries in the sample, as well as for their clustering structure. Our analysis leads to model-based estimates of country-specific endogenous age cutoffs and corresponding aging indicators. Additionally, we provide cluster-specific estimates, a novel contribution engendered by the use of hierarchical modeling, which widens the scope of our analysis beyond the countries which are present in the sample.

We introduce a fast, closed-form, simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian dynamic linear models of West and Harrison (Bayesian Forecasting and Dynamic Models (1997) Springer), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points.

Dynamic linear model analysis of optical imaging data acquired from the human neocortex

Building engineers and managers often treat occupants as passive agents despite their important role in building energy performance. Meanwhile, neglecting occupants' preferences may lead to poor implementations of building controls. The overall goal of this research is to improve buildings' energy performance and occupants' satisfaction by considering occupant-building interactions. This research first assessed the impact of static and dynamic occupant modelling approaches in a simulation-based analysis using the existing occupant models. The results showed the discrepancies between these two approaches in predicting the energy performance. The analysis emphasized the importance of using dynamic modelling approach. Given that the existing occupant models are context-specific, the next steps of this research focused on conducting a monitoring campaign. Therefore, the various monitoring methods exist in the literature and the anecdotal evidence were critically reviewed. Among the methods, environmental conditions are more controllable as well as configuring building designs and control systems are more flexible with laboratories and virtual environments. However, researchers can achieve valuable information on the natural occupants' presence and behaviour by in-situ monitoring on a relatively large sample size at a lower cost in long-term studies. This research adopted the in-situ method in a case study.

The near-shore ocean ecosystem is influenced by catchment runoff. The Great Barrier Reef has been experiencing significant water quality deterioration over the past 150 years, due in part to agricultural intensification and urban settlement in adjacent catchments (Thorburn et al., 2013). There is a need for us to understand the influences of catchment characteristics on stream water quality, with an aim to mitigate and manage the water quality issue in the terrestrial stream runoff derived from the adjacent Great Barrier Reef catchments. The event-based water quality monitoring data set from the Paddock to Reef Integrated Monitoring Program across six Natural Management Regions provides a potential opportunity to develop a data-driven understanding of catchment characteristics affecting water quality at the catchment scale. This requires a robust and reliable modelling tool to relate the monitoring data to anthropogenic and natural processes. In this study, monitoring data of Total Suspended Solids (TSS) and dissolved Oxidised Nitrogen (NOX) from 32 sites across the Great Barrier Reef catchments are selected as case study constituents due to the high risk they pose to reef health when exported from catchments to the receiving marine environment. Also, these two constituents have distinct biogeochemical processes in catchments. Specifically, TSS is typically conserved (although mobilized and deposited) while travelling through river systems; while NOX can be potentially processed and removed from the system altogether. A Bayesian hierarchical linear modelling framework is adopted, due to its ability to borrow strength among sites, allowing information to be transferred across space, and due to its ability to provide uncertainty of the predictions. The Bayesian hierarchical linear regression model in this study is developed to evaluate the significance of various catchment characteristics (e.g., land uses, catchment topography and geology) on spatial variation in water quality. The main findings of this study are listed as follows, • Sites located in the Burdekin and Fitzroy Natural Management Regions tend to have greater TSS concentrations, illustrated by the modelled site-specific spatial random effects (deviation from the overall average concentration). Additionally, grazing and dry land agriculture land uses are positively correlated with the spatial random effect on TSS. The complexity of interaction between different catchment characteristics (e.g., land use and topography) can potentially result in a negative spatial random effect on TSS, which is reduced in the relatively steeper Great Barrier Reef catchments with a denser stream network. • Sugar cane is one of the most significant NOX contributors according to the modelling results, likely partially due to the excessive application of fertilizers; however, conservation land use has limited effect on NOX removal, indicating denitrification process alone may not be sufficient to remove NOX. The modelling results demonstrate different land management strategies are required for the purpose of reducing different constituents. This work will provide scientific insights for water quality management at the catchment scale.