Journal of the American Statistical Association

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• Matching and Weighting With Functions of Error-Prone Covariates for Causal Inference AI翻译
  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 ...
• A General Regression Changepoint Test for Time Series Data AI翻译
  作者：Robbins, Michael W.; Gallagher, Colin M.; Lund, Robert B.
  作者单位：RAND Corporation
  摘要：This article develops a test for a single changepoint in a general setting that allows for correlated time series regression errors, a seasonal cycle, time-varying regression factors, and covariate information. Within, a changepoint statistic is constructed from likelihood ratio principles and its asymptotic distribution is derived. The asymptotic distribution of the changepoint statistic is shown to be invariant of the seasonal cycle and the covariates should the latter obey some simple limit...
• A Class of Functional Methods for Error-Contaminated Survival Data Under Additive Hazards Models with Replicate Measurements AI翻译
  作者：Yan, Ying; Yi, Grace Y.
  作者单位：University of Waterloo
  摘要：Covariate measurement error has attracted extensive interest in survival analysis. Since Prentice, a large number of inference methods have been developed to handle error-prone data that are modulated with proportional hazards models. In contrast to proportional hazards models, additive hazards models offer a flexible tool to delineate survival processes. However, there is little research on measurement error effects under additive hazards models. In this article, we systematically investigate...
• Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data AI翻译
  作者：Bartolucci, Francesco; Lupparelli, Monia
  作者单位：University of Perugia; University of Bologna
  摘要：In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and between clusters. For this aim, we propose an approach based on nested hidden (latent) Markov chains, which are associated with every sample unit and with every cluster. The approach allows us to account for the previously mentioned forms of unobserved heterogeneity in a dynamic fashion; it also a...
• Comment: Addressing the Need for Portability in Big Data Model Building and Calibration AI翻译
  作者：Patel, Chirag J.; Dominici, Francesca
  作者单位：Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health
• Improving and Evaluating Topic Models and Other Models of Text AI翻译
  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...
• Comment: Getting into Space with a Weight Problem AI翻译
  作者：Wakefield, Jon; Simpson, Daniel; Godwin, Jessica
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Bath
• Brownian Integrated Covariance Functions for Gaussian Process Modeling: Sigmoidal Versus Localized Basis Functions AI翻译
  作者：Zhang, Ning; Apley, Daniel W.
  作者单位：Northwestern University
  摘要：Gaussian process modeling, or kriging, is a popular method for modeling data from deterministic computer simulations, and the most common choices of covariance function are Gaussian, power exponential, and Matern. A characteristic of these covariance functions is that the basis functions associated with their corresponding response predictors are localized, in the sense that they decay to zero as the input location moves away from the simulated input sites. As a result, the predictors tend to ...
• Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification AI翻译
  作者：Forastiere, Laura; Mealli, Fabrizia; VanderWeele, Tyler J.
  作者单位：University of Florence
  摘要：Exploration of causal mechanisms is often important for researchers and policymakers to understand how an intervention works and how it can be improved. This task can be crucial in clustered encouragement designs (CEDs). Encouragement design studies arise frequently when the treatment cannot be enforced because of ethical or practical constraints and an encouragement intervention (information campaigns, incentives, etc.) is conceived with the purpose of increasing the uptake of the treatment o...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Fan, Jun; Yuan, Ming
  作者单位：University of Wisconsin System; University of Wisconsin Madison