Subsampling

Politis, Dimitris N.; Romano, Joseph P.; Wolf, Michael

doi:10.1007/978-1-4612-1554-7

Cited by 942 publications

(976 citation statements)

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“…A number of avenues of potential future work remain. For example, it would be interesting to apply our diagnostic procedure to other estimator quality assessment methods [4,17,13] and to devise extensions of the diagnostic which are suitable for variants of the bootstrap designed to handle non-i.i.d. data [9,12,14,16,18].…”

Section: Resultsmentioning

confidence: 99%

“…Unlike classical methods-which have generally relied upon analytic asymptotic approximations requiring deep analysis of specific classes of estimators in specific settings [17]-the bootstrap can be straightforwardly applied, via a simple computational mechanism, to a broad range of estimators. Since its inception, theoretical work has shown that the bootstrap is broadly consistent [3,10,20] and can be higher-order correct [11].…”

Section: Introductionmentioning

confidence: 99%

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A general bootstrap performance diagnostic

Kleiner

Talwalkar

Agarwal

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality assessment in data analysis, this task has eluded highly automatic solutions. While the bootstrap provides perhaps the most promising step in this direction, its level of automation is limited by the difficulty of evaluating its finite sample performance and even its asymptotic consistency. Thus, we present here a general diagnostic procedure which directly and automatically evaluates the accuracy of the bootstrap's outputs, determining whether or not the bootstrap is performing satisfactorily when applied to a given dataset and estimator. We show that our proposed diagnostic is effective via an extensive empirical evaluation on a variety of estimators and simulated and real datasets, including a real-world query workload from Conviva, Inc. involving 1.7TB of data (i.e., approximately 0.5 billion data points).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A general bootstrap performance diagnostic

Kleiner

Talwalkar

Agarwal

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Politis et al (1999) study uniform confidence bands for marginal distributions of stationary time series (example 3.4.7 page 89), and discuss ways to generalize bootstrap procedures to the nonstationary case (Section 4.4). A thorough review of bootstrap methods in time series is given in Haerdle et al (2003), which notes a number of shortcomings of resampling methods under dependency.…”

Section: Uncertainty Assessmentmentioning

confidence: 99%

Assessment and visualization of threshold exceedance probabilities in complex space–time settings: A case study of air quality in Northern Italy

2013

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Among the many atmospheric pollutants, fine particles are known to be particularly damaging to respiratory health, and therefore many efforts are being made worldwide to explore their spatio-temporal behavior. In this paper we focus on PM 10 , specifically addressing the probability (or risk) that such particles will exceed potentially harmful thresholds. We combine smoothing in the time domain with spatial interpolation to model threshold exceedance probabilities and their corresponding confidence regions in a flexible framework. We then present a comprehensive study of air quality in the North-Italian region Piemonte from October 2005 through March 2006. The proposed methodology, consisting of a two-stage modeling approach followed by a block bootstrap scheme, has a myriad applications to other research fields.

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“…, X i+b−1 ). Generalizations for nonstationary time series, random fields and point processes are further discussed in Politis et al (1999).…”

Section: A Subsampling Approachmentioning

confidence: 99%

Control of the False Discovery Rate Under Dependence Using the Bootstrap and Subsampling

2008

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Control of the false discovery rate under dependence using the bootstrap and subsampling Romano, J P; Shaikh, A M; Wolf, M Romano, J P; Shaikh, A M; Wolf, M (2008). Control of the false discovery rate under dependence using the bootstrap and subsampling. Abstract This paper considers the problem of testing s null hypotheses simultaneously while controlling the false discovery rate (FDR). Benjamini and Hochberg (1995) provide a method for controlling the FDR based on p-values for each of the null hypotheses under the assumption that the p-values are independent. Subsequent research has since shown that this procedure is valid under weaker assumptions on the joint distribution of the p-values.Related procedures that are valid under no assumptions on the joint distribution of the p-values have also been developed. None of these procedures, however, incorporate information about the dependence structure of the test statistics. This paper develops methods for control of the FDR under weak assumptions that incorporate such information and, by doing so, are better able to detect false null hypotheses. We illustrate this property via a simulation study and two empirical applications. In particular, the bootstrap method is competitive with methods that require independence if independence holds, but it outperforms these methods under dependence.

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Subsampling

Abstract: cm. -(Springer series in statistics) lncludes bibiiographicai references and index.

Cited by 942 publications

References 0 publications

A general bootstrap performance diagnostic

A general bootstrap performance diagnostic

Assessment and visualization of threshold exceedance probabilities in complex space–time settings: A case study of air quality in Northern Italy

Control of the False Discovery Rate Under Dependence Using the Bootstrap and Subsampling

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