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作者：Smith, Michael Stanley; Yu, Weichang; Nott, David J.; Frazier, David T.
作者单位：University of Melbourne; University of Melbourne; National University of Singapore; Monash University
摘要：In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by cutting feedback. Cutting feedback limits the influence of potentially misspecified modules in posterior inference. We consider two types of cuts. The first limits the influence of a misspecified copula on inference for the marginals, which is a Bayesian analogue of the popular In...
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作者：Halder, Aritra; Li, Didong; Banerjee, Sudipto
作者单位：Drexel University; University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Los Angeles
摘要：Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the values of the random field (or spatial surface indexed continuously over time) at arbitrary space-time coordinates, scientific interest often turns to gleaning information regarding zones of rapid spatial-temporal change. We develop Bayesian modeling and inference for directional rates of change along a given surface...
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作者：Zhang, Yi; Xu, Wenfu; Tan, Zhiqiang
作者单位：Rutgers University System; Rutgers University New Brunswick; China Jiliang University
摘要：Sensitivity analysis is important to assess the impact of unmeasured confounding in causal inference from observational studies. The marginal sensitivity model (MSM) provides a useful approach in quantifying the influence of unmeasured confounders on treatment assignment and leading to tractable sharp bounds of common causal parameters. In this article, to tighten MSM sharp bounds, we propose the enhanced MSM (eMSM) by incorporating another sensitivity constraint which quantifies the influence...
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作者：Martin, Ryan
作者单位：North Carolina State University
摘要：Inferential models (IMs) offer prior-free, Bayesian-like posterior degrees of belief designed for statistical inference, which feature a frequentist-like calibration property that ensures reliability of said inferences. The catch is that IMs' degrees of belief are possibilistic rather than probabilistic and, since the familiar Monte Carlo methods approximate probabilistic quantities, there are significant computational challenges associated with putting this framework into practice. The presen...
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作者：Li, Jiawei; Wang, Jingshen
作者单位：Boston University; Boston University
摘要：Assessing how well a Bayesian model generalizes to unobserved data is essential, yet existing general-purpose model checks are either not properly calibrated (as in posterior predictive checks) or fail to be sufficiently general for practical use (e.g., due to requiring model-specific derivations). We propose split predictive checks (SPCs) as a simple, general-purpose class of predictive checks that maintain the usability of posterior predictive checks while directly targeting predictive gener...
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作者：Seo, Won-Ki; Shang, Han Lin
作者单位：University of Sydney; Macquarie University
摘要：We develop a statistical testing procedure to examine whether the curve-valued time series of interest is integrated of order d for a nonnegative integer d. The proposed procedure can distinguish between integer-integrated time series and fractionally-integrated ones, and it has broad applicability in practice. Monte Carlo simulation experiments show that the proposed testing procedure performs reasonably well. We apply our methodology to Canadian yield curve data and French sub-national age-s...
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作者：Ke, Zheng Tracy; Kelly, Bryan; Xiu, Dacheng
作者单位：Harvard University; Yale University; National Bureau of Economic Research; University of Chicago
摘要：We develop a probabilistic framework to extract sentiment information from text by training a model to predict and rank sentiments in newly encountered documents. Our approach imposes a joint semi-parametric model on text and ordinal response variables, addressing the challenges of sparse sentiment signals and complex response distributions. Through a word screening procedure and the use of normalized ranks, our approach achieves consistent sentiment ranking without estimating the full model. ...
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作者：Kowal, Daniel R.
作者单位：Cornell University
摘要：Categorical covariates such as race, sex, or group are ubiquitous in regression analysis. While main-only (or ANCOVA) linear models are predominant, linear models that include categorical-continuous or categorical-categorical interactions are increasingly important and allow heterogeneous, group-specific effects. However, with standard approaches, the addition of categorical interactions fundamentally alters the estimates and interpretations of the main effects, often inflates their standard e...
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作者：Williams, Jonathan P.
作者单位：North Carolina State University
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作者：Ma, Haiqiang; Sheng, Zhiyan; Jiang, Jiming
作者单位：Jiangxi University of Finance & Economics; University of California System; University of California Davis
摘要：In the context of robust small area estimation (SAE), there are two types of robustness considerations, robustness against model misspecification and robustness against outliers. We propose a method of SAE that has both types of robustness features. The method combines the idea of observed best prediction (OBP), which is known to be more robust against model misspecification than the traditional best linear unbiased prediction (EBLUP) method, and the method of density power divergence (DPD), w...
