2期

最新文章

• Randomization-based Z-estimation for evaluating average and individual treatment effects AI翻译
  作者：Qu, Tianyi; Du, Jiangchuan; Li, Xinran
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago
  摘要：Randomized experiments have been the gold standard for drawing causal inference. The conventional model-based approach has been one of the most popular methods of analysing treatment effects from randomized experiments, which is often carried out through inference for certain model parameters. In this paper, we provide a systematic investigation of model-based analyses for treatment effects under the randomization-based inference framework. This framework does not impose any distributional ass...
• Semiparametric local variable selection under misspecification AI翻译
  作者：Rossell, D.; Seong, A. K.; Saez, I.; Guindani, M.
  作者单位：Pompeu Fabra University; University of California System; University of California Irvine; Icahn School of Medicine at Mount Sinai; University of California System; University of California Los Angeles
  摘要：Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of nonlinear effects and model misspecification. Specifically, for common semiparametric methods, even slight model misspecification can result in a high false positive rate, in a manner that is highly sensitive to the chosen basis functions. We propose a method based on orthogonal cut splines that avoids false positive inflation for any c...
• Efficient estimation for functional accelerated failure time models AI翻译
  作者：Liu, Changyu; Su, Wen; Liu, Kin-Yat; Yin, Guosheng; Zhao, Xingqiu
  作者单位：Chinese University of Hong Kong; City University of Hong Kong; University of Hong Kong; Hong Kong Polytechnic University
  摘要：We propose a functional accelerated failure time model to characterize the effects of both functional and scalar covariates on the time to event of interest, and provide regularity conditions to guarantee model identifiability. For efficient estimation of model parameters, we develop a sieve maximum likelihood approach where parametric and nonparametric coefficients are bundled with an unknown baseline hazard function in the likelihood function. Not only do the bundled parameters cause immense...
• Noise-induced randomization in regression discontinuity designs AI翻译
  作者：Eckles, Dean; Ignatiadis, Nikolaos; Wager, Stefan; Wu, Han
  作者单位：Massachusetts Institute of Technology (MIT); University of Chicago; University of Chicago; Stanford University
  摘要：Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a prespecified threshold. Here, we propose a new approach to identification, estimation and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable, of which the running variable is a nois...
• Sample splitting and assessing goodness-of-fit of time series AI翻译
  作者：Davis, Richard A.; Fernandes, Leon
  作者单位：Columbia University
  摘要：A fundamental and often final step in time series modelling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are intrinsically dependent since they are based on the same parameter estimates, and thus standard tests of serial independence, such as ...
• Semiparametric local variable selection under misspecification AI翻译
  作者：Rossell, D.; Seong, A. K.; Saez, I.; Guindani, M.
  作者单位：Pompeu Fabra University; University of California System; University of California Irvine; Icahn School of Medicine at Mount Sinai; University of California System; University of California Los Angeles
  摘要：Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of nonlinear effects and model misspecification. Specifically, for common semiparametric methods, even slight model misspecification can result in a high false positive rate, in a manner that is highly sensitive to the chosen basis functions. We propose a method based on orthogonal cut splines that avoids false positive inflation for any c...
• Efficient estimation for functional accelerated failure time models AI翻译
  作者：Liu, Changyu; Su, Wen; Liu, Kin-Yat; Yin, Guosheng; Zhao, Xingqiu
  作者单位：Chinese University of Hong Kong; City University of Hong Kong; University of Hong Kong; Hong Kong Polytechnic University
  摘要：We propose a functional accelerated failure time model to characterize the effects of both functional and scalar covariates on the time to event of interest, and provide regularity conditions to guarantee model identifiability. For efficient estimation of model parameters, we develop a sieve maximum likelihood approach where parametric and nonparametric coefficients are bundled with an unknown baseline hazard function in the likelihood function. Not only do the bundled parameters cause immense...
• Noise-induced randomization in regression discontinuity designs AI翻译
  作者：Eckles, Dean; Ignatiadis, Nikolaos; Wager, Stefan; Wu, Han
  作者单位：Massachusetts Institute of Technology (MIT); University of Chicago; University of Chicago; Stanford University
  摘要：Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a prespecified threshold. Here, we propose a new approach to identification, estimation and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable, of which the running variable is a nois...
• Sample splitting and assessing goodness-of-fit of time series AI翻译
  作者：Davis, Richard A.; Fernandes, Leon
  作者单位：Columbia University
  摘要：A fundamental and often final step in time series modelling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are intrinsically dependent since they are based on the same parameter estimates, and thus standard tests of serial independence, such as ...
• Consistency of common spatial estimators under spatial confounding AI翻译
  作者：Gilbert, Brian; Ogburn, Elizabeth L.; Datta, Abhirup
  作者单位：Johns Hopkins University
  摘要：This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel result on the infill consistency of the generalized ...