2021

最新文章

• Incorporating Animal Movement Into Distance Sampling AI翻译
  作者：Glennie, R.; Buckland, S. T.; Langrock, R.; Gerrodette, T.; Ballance, L. T.; Chivers, S. J.; Scott, M. D.
  作者单位：University of St Andrews; University of Bielefeld; National Oceanic Atmospheric Admin (NOAA) - USA
  摘要：Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an expl...
• Training Neural Networks as Learning Data-Adaptive Kernels: Provable Representation and Approximation Benefits AI翻译
  作者：Dou, Xialiang; Liang, Tengyuan
  作者单位：University of Chicago; University of Chicago
  摘要：Consider the problem: given the data pair drawn from a population with , specify a neural network model and run gradient flow on the weights over time until reaching any stationarity. How does f(t), the function computed by the neural network at time t, relate to , in terms of approximation and representation? What are the provable benefits of the adaptive representation by neural networks compared to the prespecified fixed basis representation in the classical nonparametric literature? We ans...
• Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection AI翻译
  作者：Lin, Kevin Z.; Liu, Han; Roeder, Kathryn
  作者单位：Carnegie Mellon University; Northwestern University
  摘要：Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, guilt by association methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to aff...
• The Augmented Synthetic Control Method AI翻译
  作者：Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse
  作者单位：Harvard University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The synthetic control is a weighted average of control units that balances the treated unit's pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pretreatment...
• Comment on A Gibbs Sampler for a Class of Random Convex Polytopes by PE Jacob, R. Gong, PT Edlefsen and AP Dempster AI翻译
  作者：Ruggeri, Fabrizio
  作者单位：Consiglio Nazionale delle Ricerche (CNR); Istituto di Matematica Applicata e Tecnologie Informatiche Enrico Magenes (IMATI-CNR)
• Inference for Multivariate Regression Model Based on Synthetic Data Generated Using Plug-in Sampling AI翻译
  作者：Moura, Ricardo; Klein, Martin; Zylstra, John; Coelho, Carlos A.; Sinha, Bimal
  作者单位：Universidade Nova de Lisboa; US Food & Drug Administration (FDA); University System of Maryland; University of Maryland Baltimore County; Universidade Nova de Lisboa
  摘要：In this article, the authors derive the likelihood-based exact inference for singly and multiply imputed synthetic data in the context of a multivariate regression model. The synthetic data are generated via the Plug-in Sampling method, where the unknown parameters in the model are set equal to the observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Simulation studies are carried out in order to confirm th...
• Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies AI翻译
  作者：Lee, Kwonsang; Small, Dylan S.; Dominici, Francesca
  作者单位：Sungkyunkwan University (SKKU); University of Pennsylvania; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this article, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses randomization-based tests to as...
• A Bayesian State-Space Approach to Mapping Directional Brain Networks AI翻译
  作者：Li, Huazhang; Wang, Yaotian; Yan, Guofen; Sun, Yinge; Tanabe, Seiji; Liu, Chang-Chia; Quigg, Mark S.; Zhang, Tingting
  作者单位：University of Virginia; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Virginia; University of Virginia; University of Virginia; University of Virginia
  摘要：The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use intracranial electroencephalography (iEEG) to record the patient's intracranial brain activity in many small regions. iEEG data are high-dimensional multivariate time series. We...
• Efficient Estimation of Optimal Regimes Under a No Direct Effect Assumption AI翻译
  作者：Liu, Lin; Shahn, Zach; Robins, James M.; Rotnitzky, Andrea
  作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; International Business Machines (IBM); IBM USA; Harvard University; Universidad Torcuato Di Tella; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
  摘要：We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no effect on a patient's clinical outcomes except through the effect of the test results on the choice of treatment. We model the optimal joint strategy with an optimal structural nested mean model (opt-SNMM). The proposed estimators are more efficient than previous estimators of the parameters of an opt-SNMM because th...
• Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs AI翻译
  作者：Cattaneo, Matias D.; Keele, Luke; Titiunik, Rocio; Vazquez-Bare, Gonzalo
  作者单位：Princeton University; University of Pennsylvania; Princeton University; University of California System; University of California Santa Barbara
  摘要：In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-int...