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作者：Dong, Yingying; Lee, Ying-Ying; Gou, Michael
作者单位：University of California System; University of California Irvine
摘要：The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This article establishes identification and robust bias-corrected inference for such RD designs. Causal identification is achieved by using any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). We discuss a double-robust identification approach and propose an estim...
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作者：Liu, Hua; You, Jinhong; Cao, Jiguo
作者单位：Xi'an Jiaotong University; Shanghai Lixin University of Accounting & Finance; Shanghai University of Finance & Economics; Simon Fraser University
摘要：Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model. The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response. The proposed model includes many important models investigated recently as special cases. By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step e...
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作者：Zhu, Wanrong; Chen, Xi; Wu, Wei Biao
作者单位：University of Chicago; New York University; University of Chicago
摘要：The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge datasets and online learning. While this recursive algorithm is popular for computation and memory efficiency, quantifying variability and randomness of the solutions has been rarely studied. This article aims at conducting statistical inference of SGD-based estimates in an online setting. In particular, we propose a fully online estimator for the covariance matrix of averaged SGD (ASGD...
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作者：Gabriel, Erin E.; Sjolander, Arvid; Sachs, Michael C.
作者单位：Karolinska Institutet
摘要：Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments, making the intervention effect that the experiment was designed to estimate nonidentifiable. Nonparametric causal bounds provide a way to narrow the range of possible values for a nonidentifiable causal effect with minimal assumptions. We derive novel bounds for the causal risk difference for a binary outcome and intervention in randomized experiments with nonignorable missingness that is caused ...
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作者：Ke, Zheng Tracy; Ma, Yucong; Lin, Xihong
作者单位：Harvard University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, K. For estimation of K, most attention has focused on the use of top eigenvalues of sample covariance matrix, and there is little investigation into proper ways of using bulk eigenvalues to estimate K. We propose a principled approach to incorporating bulk eigenvalues in the estimation of K. Our method imposes a working mode...
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作者：Reluga, Katarzyna; Lombardia, Maria-Jose; Sperlich, Stefan
作者单位：University of Cambridge; Universidade da Coruna; University of Geneva
摘要：Today, generalized linear mixed models (GLMM) are broadly used in many fields. However, the development of tools for performing simultaneous inference has been largely neglected in this domain. A framework for joint inference is indispensable to carry out statistically valid multiple comparisons of parameters of interest between all or several clusters. We therefore develop simultaneous confidence intervals and multiple testing procedures for empirical best predictors under GLMM. In addition, ...
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作者：Drechsler, Joerg
作者单位：University System of Maryland; University of Maryland College Park
摘要：Government agencies typically need to take potential risks of disclosure into account whenever they publish statistics based on their data or give external researchers access to collected data. In this context, the promise of formal privacy guarantees offered by concepts such as differential privacy seems to be the panacea enabling the agencies to quantify and control the privacy loss incurred by any data release exactly. Nevertheless, despite the excitement in academia and industry, most agen...
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作者：Chan, Kwun Chuen Gary
作者单位：University of Washington; University of Washington Seattle
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作者：Jia, Yisu; Kechagias, Stefanos; Livsey, James; Lund, Robert; Pipiras, Vladas
作者单位：State University System of Florida; University of North Florida; SAS Institute Inc; University of California System; University of California Santa Cruz; University of North Carolina; University of North Carolina Chapel Hill
摘要：This article develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any prespecified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule-Walker estimation paradigms, ba...
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作者：Imai, Kosuke; Li, Michael Lingzhi
作者单位：Harvard University; Harvard University; Massachusetts Institute of Technology (MIT)
摘要：The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a target population. We propose a new evaluation metric, the population average prescriptive effect (PAPE). The PAPE compares the performance of ITR with that of non-individualized treatment rule, which randomly treats the same pro...
