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作者：Xu, Jiazhen; Wood, Andrew T. A.; Zou, Tao
作者单位：Australian National University
摘要：Functional data analysis offers a diverse toolkit of statistical methods tailored to analysing samples of real-valued random functions. Recently, samples of time-varying random objects, such as time-varying networks, have been increasingly encountered in data analysis. These data structures represent elements within general metric spaces that lack local or global linear structures, rendering traditional functional data analysis methods inapplicable. Moreover, the existing methodology for time-...
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作者：Gelman, Andrew; Mikhaeil, Jonas M.
作者单位：Columbia University; Columbia University
摘要：It has been proposed in medical decision analysis to express the 'first, do no harm' principle as an asymmetric utility function in which the loss from killing a patient would count more than the gain from saving a life. Such a utility depends on unrealized potential outcomes, and we show how this yields a paradoxical decision recommendation in a simple hypothetical example involving games of Russian roulette. The problem is resolved if we abandon the stable unit treatment value assumption and...
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作者：Schervish, M. J.; Kadane, J. B.; Seidenfeld, T.; Stern, R. B.
作者单位：Carnegie Mellon University; Carnegie Mellon University; Universidade de Sao Paulo
摘要：We study the amount of Fisher information about the odds ratio parameter that one loses by conditioning on the random margin totals of a $ 2\times 2 $ table for the cases in which the data arise either as a multinomial sample or as two independent binomial samples. When there is a nuisance parameter, as in this problem, many authors have proposed the concept of partial information to quantify the amount of Fisher information about the parameter of interest. We show that, as the sample size of ...
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作者：Li, Xinran
作者单位：University of Chicago
摘要：Observational studies provide invaluable opportunities to draw causal inference, but they may suffer from biases due to pretreatment differences between treated and control units. Matching is a popular approach to reduce observed covariate imbalance. To tackle unmeasured confounding, a sensitivity analysis is often conducted to investigate how robust a causal conclusion is to the strength of unmeasured confounding. For matched observational studies, Rosenbaum proposed a sensitivity analysis fr...
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作者：Mauri, L.; Dunson, D. B.
作者单位：Duke University
摘要：This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors in the linear predictor. Bayesian approaches are useful for characterizing uncertainty in inferring the regression coefficients, factors and loadings, while also incorporating hierarchical and shrinkage structures. However, Markov chain Monte Carlo algorithms for posterior computation face challenges in scaling to h...
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作者：Gui, Lin; Jiang, Yuchao; Wang, Jingshu
作者单位：University of Chicago; Texas A&M University System; Texas A&M University College Station
摘要：Combining dependent $ p $-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, $ p $-value combination tests based on regularly-varying-tailed distributions, such as the Cauchy combination test and harmonic mean $ p $-value, have attracted attention for their robustness to unknown dependence. This paper provides a theoretical and empirical evaluation of these methods under an asympto...
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作者：Li, Jinming; Xu, Gongjun; Zhu, Ji
作者单位：University of Michigan System; University of Michigan
摘要：Factor analysis is a statistical tool widely used in many disciplines, such as psychology, economics and sociology. As observations linked by networks become increasingly common, incorporating network structures into factor analysis is an important problem that remains open. This article focuses on high-dimensional factor analysis involving network-connected observations, and we propose a generalized factor model with latent factors that account for both the network structure and the dependenc...
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作者：Sakai, Mana; Matsuda, Takeru; Kubokawa, Tatsuya
作者单位：University of Tokyo; University of Tokyo
摘要：Asymptotically unbiased priors, introduced by Hartigan (1965), are designed to achieve second-order unbiasedness of Bayes estimators. This paper extends Hartigan's framework to non-independent-and-identically-distributed models by deriving a system of partial differential equations that characterizes asymptotically unbiased priors. Furthermore, we establish a necessary and sufficient condition for the existence of such priors and propose a simple procedure for constructing them. The proposed m...
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作者：Stolf, F.; Dunson, D. B.
作者单位：Duke University
摘要：Joint species distribution models are popular in ecology for modelling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multivariate binary indicators of the occurrences of different species in each sample, along with sample-specific covariates. A key problem is that current models implicitly assume that the list of species under consideration is predefined and finite, while for highly diverse groups of organisms, it is impossible to antic...
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作者：Bhattacharya, Sohom; Mukherjee, Rajarshi; Ogburn, Elizabeth L.
作者单位：State University System of Florida; University of Florida; Harvard University; Harvard T.H. Chan School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：identified the issue of ' nonsense correlations' in time series data, where dependence within each of two random vectors causes overdispersion, i.e., variance inflation, for measures of dependence between the two. Since then much has been written about nonsense correlations, but nearly all of it confined to the time series literature. In this paper we provide the first, to our knowledge, rigorous study of this phenomenon for other forms of (positive) dependence, specifically for Markov random ...
