Featured Cover MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew D.
Journal of applied econometrics,
August 2023, 2023-08-00, 20230801, Volume:
38, Issue:
5
Journal Article
Peer reviewed
Open access
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.
In the last decades, the world's energy consumption has increased rapidly due to fundamental changes in the industry and economy. In such terms, accurate demand forecasts are imperative for decision ...makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. This paper expands the fields of application of combined Bootstrap aggregating (Bagging) and forecasting methods to the electric energy sector, a novelty in literature, in order to obtain more accurate demand forecasts. A comparative out-of-sample analysis is conducted using monthly electric energy consumption time series from different countries. The results show that the proposed methodologies substantially improve the forecast accuracy of the demand for energy end-use services in both developed and developing countries. Findings and policy implications are further discussed.
•Electricity demand across different countries is forecasted 24 months in advance.•The potential gains of using bagging techniques to enhance forecasts are explored.•A new variation of a bagging procedure is proposed.•The proposed techniques provided consistently accurate forecasts in most cases.
Comment Shah, Rajen D; Samworth, Richard J
Journal of the American Statistical Association,
12/2015, Volume:
110, Issue:
512
Journal Article
Peer reviewed
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.
The present study examined the mediating role of intolerance of uncertainty and fear of COVID-19 in the relationship between self-compassion and well-being. The participants were comprised of 667 ...Turkish individuals (465 females and 202 males; aged between 18 and 73 years) from 75 of 81 cities in Turkey. The model was investigated using bootstrapping. The results showed that self-compassion, intolerance of uncertainty, fear of COVID-19, and well-being are significantly interrelated. Moreover, a serial mediation was found among the variables: individuals with a growth self-compassion to report lower intolerance of uncertainty, which further decreased perceived fear of COVID-19, and subsequently weakened well-being. Results are discussed in the context of COVID-19 and the well-being literature, and theoretical and practical implications were also provided.
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