A review of distributed statistical inference

Gao, Yuan; Liu, Weidong; Wang, Hansheng; Wang, Xiaozhou; Yan, Yibo; Zhang, Riquan

doi:10.1080/24754269.2021.1974158

Cited by 25 publications

(13 citation statements)

References 43 publications

(50 reference statements)

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“…Both the MPLE and MLE of the ERGM are consistent for a growing number of networks observed from the same set of fixed nodes (Arnold & Strauss, 1991). The MPLE can be rapidly computed from large networks (Schmid & Desmarais, 2017), if necessary, using divide-and-conquer (Gao et al 2022;Rosenblatt & Nadler, 2016;Minsker, 2019) or streaming methods (Luo & Song, 2019).…”

Section: Overview Of Network Data Modelingmentioning

confidence: 89%

Approximate Bayesian computation using asymptotically normal point estimates

Karabatsos¹

2022

Comput Stat

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This paper introduces a novel Approximate Bayesian Computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihood functions. This framework can describe the possibly skewed and high dimensional posterior distribution by a novel multivariate copula-based distribution, based on univariate marginal posterior distributions which can account for skewness and be accurately estimated by Distribution Random Forests (DRF) while performing automatic summary statistics (covariates) selection, and on robustly estimated copula dependence parameters. The framework employs a novel multivariate mode estimator to perform for MLE and posterior mode estimation, and provides an optional step to perform model selection from a given set of models with posterior probabilities estimated by DRF. The posterior distribution estimation accuracy of the ABC framework is illustrated through simulation studies involving models with analytically computable posterior distributions, and involving exponential random graph and mechanistic network models which are each defined by an intractable likelihood from which it is costly to simulate large network datasets. Also, the framework is illustrated through analyses of large reallife networks of sizes ranging between 28,000 to 65.6 million nodes (between 3 million to 1.8 billion edges), including a large multilayer network with weighted directed edges.

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Section: Overview Of Network Data Modelingmentioning

confidence: 89%

Approximate Bayesian computation using asymptotically normal point estimates

Karabatsos¹

2022

Comput Stat

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“…Offline or Online Distributed (Parameter) Estimation. One of the extensively studied distributed estimation settings is the offline setting [22], where each agent obtains multiple i.i.d. samples at the beginning of the learning task.…”

Section: Related Workmentioning

confidence: 99%

To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?

Chen

Menasché

Towsley

2021

2021 55th Annual Conference on Information Sciences and Systems (CISS)

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We study sensor/agent data collection and collaboration policies for parameter estimation, accounting for resource constraints and correlation between observations collected by distinct sensors/agents. Specifically, we consider a group of sensors/agents each samples from different variables of a multivariate Gaussian distribution and has different estimation objectives, and we formulate a sensor/agent's data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. When the knowledge of correlation between variables is available, we analytically identify two particular scenarios: (1) where the knowledge of the correlation between samples cannot be leveraged for collaborative estimation purposes and (2) where the optimal data collection policy involves investing scarce resources to collaboratively sample and transfer information that is not of immediate interest and whose statistics are already known, with the sole goal of increasing the confidence on the estimate of the parameter of interest. When the knowledge of certain correlation is unavailable but collaboration may still be worthwhile, we propose novel ways to apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our distributed parameter estimation problem and demonstrate that the proposed algorithms, DOUBLE-F, DOUBLE-Z, UCB-F, UCB-Z, are effective through simulations.

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“…The literature on distributed learning is flourishing and encompasses a variety of topics: M -estimation (Zhang et al, 2013, Shamir et al, 2014, Lee et al, 2017, Wang et al, 2017, Battey et al, 2018, Jordan et al, 2019, Fan et al, 2021, Principal Component Analysis (Garber et al, 2017, Fan et al, 2019, Chen et al, 2021, feature screening (Li et al, 2020), to name a few. We refer to Gao et al (2022) for a recent review of the distributed learning literature.…”

Section: Introductionmentioning

confidence: 99%

Communication-Efficient Distributed Estimation and Inference for Cox's Model

Bayle¹,

Fan²,

Lou³

2023

Preprint

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Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals for linear combinations of high-dimensional hazard regression coefficients, we introduce a novel debiased method, establish central limit theorems, and provide consistent variance estimators that yield asymptotically valid distributed confidence intervals. In addition, we provide valid and powerful distributed hypothesis tests for any coordinate element based on a decorrelated score test. We allow time-dependent covariates as well as censored survival times. Extensive numerical experiments on both simulated and real data lend further support to our theory and demonstrate that our communicationefficient distributed estimators, confidence intervals, and hypothesis tests improve upon alternative methods.

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A review of distributed statistical inference

Cited by 25 publications

References 43 publications

Approximate Bayesian computation using asymptotically normal point estimates

Approximate Bayesian computation using asymptotically normal point estimates

To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?

Communication-Efficient Distributed Estimation and Inference for Cox's Model

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