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作者：Airoldi, Edoardo M.
作者单位：Harvard University
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作者：Haerdle, Wolfgang Karl; Cabrera, Brenda Lopez; Okhrin, Ostap; Wang, Weining
作者单位：Humboldt University of Berlin; Technische Universitat Dresden
摘要：On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of t...
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作者：Rosenblum, Michael
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
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作者：Gaskins, J. T.; Daniels, M. J.; Marcus, B. H.
作者单位：University of Louisville; University of Texas System; University of Texas Austin; University of California System; University of California San Diego
摘要：Inference on data with missingness can be challenging, particularly if the knowledge that a measurement was unobserved provides information about its distribution. Our work is motivated by the Commit to Quit II study, a smoking cessation trial that measured smoking status and weight change as weekly outcomes. It is expected that dropout in this study was informative and that patients with missed measurements are more likely to be smoking, even after conditioning on their observed smoking and w...
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作者：Taddy, Matt
作者单位：Microsoft; University of Chicago
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作者：Jing, Bing-Yi; Li, Zhouping; Pan, Guangming; Zhou, Wang
作者单位：Hong Kong University of Science & Technology; Lanzhou University; Nanyang Technological University; National University of Singapore
摘要：The article is concerned with empirical Bayes shrinkage estimators for the heteroscedastic hierarchical normal model using Stein's unbiased estimate of risk (SURE). Recently, Xie, Kou, and Brown proposed. a class of estimators for this type of problems and established their asymptotic optimality properties under the assumption of known but unequal variances. In this article, we consider this problem with unequal and unknown variances, which may be more appropriate in real situations. By placin...
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作者：Lockwood, J. R.; McCaffrey, Daniel F.
作者单位：Educational Testing Service (ETS)
摘要：Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard (i.e., no measurement ...
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作者：Airoldi, Edoardo M.; Bischof, Jonathan M.
作者单位：Harvard University; Alphabet Inc.; Google Incorporated
摘要：An ongoing challenge in the analysis of document collections is how to summarize content in terms of a set of inferred themes that can be interpreted substantively in terms of topics. The current practice of parameterizing the themes in terms of most frequent words limits interpretability by ignoring the differential use of words across topics. Here, we show that words that are both frequent and exclusive to a theme are more effective at characterizing topical content, and we propose a regular...
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作者：Fan, Jun; Yuan, Ming
作者单位：University of Wisconsin System; University of Wisconsin Madison
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作者：Rockova, Veronika; George, Edward I.
作者单位：University of Chicago; University of Pennsylvania
摘要：Rotational post hoc transformations have traditionally played a key role in enhancing the interpretability of factor analysis. Regularization methods also serve to achieve this goal by prioritizing sparse loading matrices. In this work, we bridge these two paradigms with a unifying Bayesian framework. Our approach deploys intermediate factor rotations throughout the learning process, greatly enhancing the effectiveness of sparsity inducing priors. These automatic rotations to sparsity are embe...
