Evaluation of Combining Bootstrap with Multiple Imputation Using R on Knights Landing Platform

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

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Multiple imputation strategies for a bounded outcome variable in a competing risks analysis

Statistics in Medicine ◽

10.1002/sim.8879 ◽

2021 ◽

Author(s):

Elinor Curnow ◽

Rachael A. Hughes ◽

Kate Birnie ◽

Michael J. Crowther ◽

Margaret T. May ◽

...

Keyword(s):

Multiple Imputation ◽

Competing Risks ◽

Outcome Variable ◽

Competing Risks Analysis

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Parametric bootstrap estimators for hybrid inference in forest inventories

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpx048 ◽

2017 ◽

Vol 91 (3) ◽

pp. 354-365 ◽

Cited By ~ 7

Author(s):

Mathieu Fortin ◽

Rubén Manso ◽

Robert Schneider

Keyword(s):

Multiple Imputation ◽

Nonlinear Models ◽

Parametric Bootstrap ◽

Simulation Studies ◽

Variance Estimator ◽

Resampling Methods ◽

Forest Inventories ◽

Point Estimator ◽

Complex Models ◽

High Level

Abstract In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models.

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