Divide-and-Conquer Information-Based Optimal
            Subdata Selection Algorithm

Wang, Haiying

doi:10.1007/s42519-019-0048-5

Cited by 22 publications

(6 citation statements)

References 14 publications

(33 reference statements)

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“…For modeling with interactions, OSS is significantly faster than IBOSS because OSS chooses subsamples only based on covariates while IBOSS relies also on the interaction terms. It should be noted that IBOSS (Wang, 2019) and OSS can both be further accelerated with parallel computing, making the selection and analysis of the subsamples much more efficient. 5.4.…”

Section: Computing Timementioning

confidence: 99%

Orthogonal Subsampling for Big Data Linear Regression

Wang¹,

Elmstedt²,

Wong³

et al. 2021

Preprint

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The dramatic growth of big datasets presents a new challenge to data storage and analysis. Data reduction, or subsampling, that extracts useful information from datasets is a crucial step in big data analysis. We propose an orthogonal subsampling (OSS) approach for big data with a focus on linear regression models. The approach is inspired by the fact that an orthogonal array of two levels provides the best experimental design for linear regression models in the sense that it minimizes the average variance of the estimated parameters and provides the best predictions. The merits of OSS are three-fold: (i) it is easy to implement and fast; (ii) it is suitable for distributed parallel computing and ensures the subsamples selected in different batches have no common data points; and (iii) it outperforms existing methods in minimizing the mean squared errors of the estimated parameters and maximizing the efficiencies of the selected subsamples. Theoretical results and extensive numerical results show that the OSS approach is superior to existing subsampling approaches. It is also more robust to the presence of interactions among covariates and, when they do exist, OSS provides more precise estimates of the interaction effects than existing methods. The advantages of OSS are also illustrated through analysis of real data.

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Section: Computing Timementioning

confidence: 99%

Orthogonal Subsampling for Big Data Linear Regression

Wang¹,

Elmstedt²,

Wong³

et al. 2021

Preprint

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show abstract

“…In this context, there are many subset selection methods, e.g. for linear models (Wang (2019)) or Bayesian system identification (Green (2015)). Recently, Peter and Nelles (2019) presented a more holistic approach, which selects points by targeting an arbitrary probability density func-tion of the subset.…”

Section: Related Workmentioning

confidence: 99%

Space-Filling Subset Selection for an Electric Battery Model

Gesner,

Gletter,

Landenberger

et al. 2020

Preprint

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Dynamic models of the battery performance are an essential tool throughout the development process of automotive drive trains. The present study introduces a method making a large data set suitable for modeling the electrical impedance. When obtaining data-driven models, a usual assumption is that more observations produce better models. However, real driving data on the battery's behavior represent a strongly non-uniform excitation of the system, which negatively affects the modeling. For that reason, a subset selection of the available data was developed. It aims at building accurate nonlinear autoregressive exogenous (NARX) models more efficiently. The algorithm selects those dynamic data points that fill the input space of the nonlinear model more homogeneously. It is shown, that this reduction of the training data leads to a higher model quality in comparison to a random subset and a faster training compared to modeling using all data points.

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“…Additionally, the DAC strategy has been extended to a sparse Cox regression by Wang et al (2019); Xue et al (2020) developed a DAC algorithm that updates test statistics for hypothesis testing of the proportional hazards assumption under the Cox model as blocks of data are received sequentially. Moreover, the DAC algorithm has been incorporated in many existing techniques from various fields to improve precision and efficiency, namely, the evolutionary algorithm for large-scale optimization (Yang, Tang & Yao, 2019), information-based optimal subdata selection algorithm (Wang, 2019), a coevolutionary algorithm to enhance resource allocation for better control of a spreading virus (Zhao et al, 2020), and precision oncology for subtypes of sarcoma (Pestana et al, 2020), among others.…”

Section: Introductionmentioning

confidence: 99%

Divide and conquer for accelerated failure time model with massive time‐to‐event data

Yin

Zhang

et al. 2022

Can J Statistics

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Big data present new theoretical and computational challenges as well as tremendous opportunities in many fields. In health care research, we develop a novel divide‐and‐conquer (DAC) approach to deal with massive and right‐censored data under the accelerated failure time model, where the sample size is extraordinarily large and the dimension of predictors is large but smaller than the sample size. Specifically, we construct a penalized loss function by approximating the weighted least squares loss function by combining estimation results without penalization from all subsets. The resulting adaptive LASSO penalized DAC estimator enjoys the oracle property. Simulation studies demonstrate that the proposed DAC procedure performs well and also reduces the computation time with satisfactory performance compared with estimation results using the full data. Our proposed DAC approach is applied to a massive dataset from the Chinese Longitudinal Healthy Longevity Survey.

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Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm

Cited by 22 publications

References 14 publications

Orthogonal Subsampling for Big Data Linear Regression

Orthogonal Subsampling for Big Data Linear Regression

Space-Filling Subset Selection for an Electric Battery Model

Divide and conquer for accelerated failure time model with massive time‐to‐event data

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