In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper develops a semiparametric TVP vector autoregression (VAR) using Bayesian additive regression trees (BART) that models the TVPs as an unknown function of effect modifiers. The novelty of this model arises from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to U.S. macroeconomic data, we illustrate the use of our model in tracking the evolving nature of the Phillips curve trade-off between inflation and unemployment in response to business cycle shocks.
This paper proposes a two-tier hybrid framework for the early detection of fake news in fast-moving, time-dependent online environments. At the first level, lexicon-based sentiment analysis is used to monitor the emotional and sentiment structure of news articles, capturing within-text trajectories and identifying patterns of emotional dissonance that distinguish manually fabricated from genuine reports. At the second level, the maximum entropy bootstrap (MEB) is combined with an ensemble of forecasting models—exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and extreme learning machines (ELM)—to model keyword-based time-series data from Google Trends and to construct one-sided predictive intervals for public-attention dynamics. Anomalies in keyword attention are then identified via bootstrap-based confidence bounds, without relying on Gaussian error assumptions, and interpreted as potential early-warning signals of misinformation-related shocks. The framework is evaluated on a curated corpus of 25 news articles (20 authentic and five manually fabricated), on synthetic stress tests with engineered attention spikes, and on independently documented misinformation episodes involving keywords such as “Obama birth certificate,” “covid 5g,” and “pizzagate.” Empirical results show that the hybrid system reliably separates fake from genuine texts in the controlled corpus and consistently flags crisis-like surges in keyword attention, providing an interpretable, statistically grounded decision-support tool for early-stage misinformation monitoring that integrates semantic analysis with robust time-series modeling.
Important biomarkers are routinely measured during patients’ follow-up visits to monitor their response to treatment and track disease progression. Accurate, individualized dynamic prediction based on data available at these visits is essential for making informed decisions regarding the next stage of treatment. We propose a versatile landmark model designed to address the complexities of real-world clinical scenarios, including (1) complex longitudinal biomarker trajectories not following a prespecified parametric function over time, (2) the potential for a fraction of patients to be cured, and (3) the violation of the proportional hazards assumption in the relationship between predictors and cure or survival outcomes. The proposed landmark model leverages functional principal component analysis (FPCA) to extract predictive features from longitudinal biomarker data, using the resulting functional principal component scores as predictors. Simulation studies demonstrate that the proposed model outperforms commonly used prediction models in terms of AUC values and Brier scores across a range of complex scenarios. We illustrate the practical utility of our model through its application to a study of chronic myeloid leukemia, showcasing its effectiveness in dynamically predicting individual conditional cure and survival probabilities.
We propose a novel Bayesian approach to estimate causal effects in spatially clustered survival data. Using Soft Bayesian Additive Regression Trees (SBART), we introduce a nonparametric regression for a log-Normal survival model that accommodates spatial associations among unknown cluster effects through a directed acyclic graph autoregressive (DAGAR) model. We employ a two-stage approach, which entails estimating the propensity score in the first step and incorporating it as a confounder of the outcome model in the second step. In our simulation study, we compare our method with existing approaches under various simulation scenarios, including both correctly specified and misspecified outcome models, to demonstrate the superior performance of our method. We then apply our method to analyze the causal effect of treatment delay (TD) on posttreatment survival of breast cancer patients from the Florida Cancer Registry (FCR). Our analysis produces the county-specific as well as state-wide assessment of the causal effects while accommodating spatial association among counties.
The integrative association test utilizes a weighting scheme to combine prior information and increase statistical power. In whole-genome sequencing (WGS) studies, it facilitates the integration of biological characteristics of single nucleotide variants (SNVs) to improve the detection of novel disease genes. Despite recent applied advances, determining the best weights to fully leverage relevant information remains an open question. For a broad family of weighted integrative tests, this paper proposes optimized weights that maximize the tests’ asymptotic efficiency, a dominant metric influencing statistical power. The study elucidates how weighting enhances statistical power and designs a practical approach for integrating effective information from SNV allele frequencies, annotations, and linkage disequilibrium. Extensive simulations demonstrate improved statistical power compared to existing methods. An osteoporosis case study further illustrates the method’s application and potential for detecting additional novel disease genes.
