Featured Cover MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew D.
Journal of applied econometrics (Chichester, England),
August 2023, 2023-08-00, 20230801, Letnik:
38, Številka:
5
Journal Article
Recenzirano
The cover image is based on the Research Article Fast and reliable jackknife and bootstrap methods for cluster‐robust inference by James G. MacKinnon et al., https://doi.org/10.1002/jae.2969.
Health disparity research often evaluates health outcomes across demographic subgroups. Multilevel regression and poststratification (MRP) is a popular approach for small subgroup estimation as it ...can stabilize estimates by fitting multilevel models and adjust for selection bias by poststratifying on auxiliary variables, which are population characteristics predictive of the analytic outcome. However, the granularity and quality of the estimates produced by MRP are limited by the availability of the auxiliary variables' joint distribution; data analysts often only have access to the marginal distributions. To overcome this limitation, we embed the estimation of population cell counts needed for poststratification into the MRP workflow: embedded MRP (EMRP). Under EMRP, we generate synthetic populations of the auxiliary variables before implementing MRP. All sources of estimation uncertainty are propagated with a fully Bayesian framework. Through simulation studies, we compare different methods of generating the synthetic populations and demonstrate EMRP's improvements over alternatives on the bias‐variance tradeoff to yield valid subpopulation inferences of interest. We apply EMRP to the Longitudinal Survey of Wellbeing and estimate food insecurity prevalence among vulnerable groups in New York City. We find that all EMRP estimators can correct for the bias in classical MRP while maintaining lower standard errors and narrower confidence intervals than directly imputing with the weighted finite population Bayesian bootstrap (WFPBB) and design‐based estimates. Performances from the EMRP estimators do not differ substantially from each other, though we would generally recommend using the WFPBB‐MRP for its consistently high coverage rates.
The R package treeclim helps perform numerical calibration of proxy-climate relationships, with an emphasis on tree-ring chronologies. The package provides a unified, fast, and public-domain ...compilation of established methods while adding novel functionality not implemented in other software. treeclim includes static and moving bootstrapped response and correlation functions, seasonal correlation analysis, a test for spurious temporal changes in proxy-climate relations, and the evaluation of reconstruction skills. The stationary bootstrap method has been incorporated into the program as a ‘blocks of blocks’ resampling scheme. Applications of treeclim include the calibration of proxy timeseries used in paleoclimatology, forest ecology, and environmental monitoring.
Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a ...computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira-Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66-33.3) to 10.2 (range: 1.32-41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.
Comment Shah, Rajen D; Samworth, Richard J
Journal of the American Statistical Association,
12/2015, Letnik:
110, Številka:
512
Journal Article
Recenzirano
Two potential screening procedures constructed by modifying the adaptive resampling test (ART): (1) a "parametric bootstrap" analog of ART; and (2) an ART-inspired adaptive testing procedure designed ...to be more powerful against dense, weak alternatives are presented in this article. The parametric bootstrap procedure avoids the tuning parameter used in ART and thus eliminates potentially computationally burdensome tuning. The proposed parametric bootstrap procedure has a desirable invariance property under local alternatives. However, both ART and proposed parametric bootstrap analog can have poor power against dense, weak alternatives. A class of adaptive procedures that reduce to our parametric bootstrap version of ART under strong, sparse signals and reduce to a sum of squares criteria under weak, dense signals are proposed.
Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be ...accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.
Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the ...case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.
We propose a new approach to the problem of high-dimensional multivariate ANOVA via bootstrapping max statistics that involve the differences of sample mean vectors. The proposed method proceeds via ...the construction of simultaneous confidence regions for the differences of population mean vectors. It is suited to simultaneously test the equality of several pairs of mean vectors of potentially more than two populations. By exploiting the variance decay property that is a natural feature in relevant applications, we are able to provide dimension-free and nearly parametric convergence rates for Gaussian approximation, bootstrap approximation, and the size of the test. We demonstrate the proposed approach with ANOVA problems for functional data and sparse count data. The proposed methodology is shown to work well in simulations and several real data applications.
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