Flood frequency analysis: Confidence interval estimation by test inversion bootstrapping

Schendel, Thomas; Thongwichian, Rossukon

doi:10.1016/j.advwatres.2015.05.004

Cited by 13 publications

(5 citation statements)

References 51 publications

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“…However, in certain situations, they have been found to be the most accurate, particularly for estimating parameters and return levels from extreme-value distributions for the stationary case (cf. Schendel andThongwichian 2015, 2017a,b).…”

Section: (Vi) Test Inversion Bootstrap CImentioning

confidence: 99%

“…The test-inversion bootstrap (TIB) presents a very different procedure for finding CI's from those discussed above. The idea is to take advantage of the duality between test inversion and CI's (Garthwaite and Buckland 1992;Kabaila 1993;Carpenter 1999;Carpenter and Bithell 2000;Schendel andThongwichian 2015, 2017a,b). That is, the correct endpoints, u U and u L for a (1 2 a) 3 100% CI (u L , u U ) must satisfy Fû( û; u U , c) 5 1 2 a/2 and Fû( û; u L , c) 5 a/2, where c represents one or more nuisance parameters.…”

Section: (Vi) Test Inversion Bootstrap CImentioning

confidence: 99%

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Bootstrap Methods for Statistical Inference. Part I: Comparative Forecast Verification for Continuous Variables

Gilleland

2020

Journal of Atmospheric and Oceanic Technology

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When making statistical inferences, bootstrap resampling methods are often appealing because of less stringent assumptions about the distribution of the statistic(s) of interest. However, the procedures are not free of assumptions. This paper addresses a specific situation that occurs frequently in atmospheric sciences where the standard bootstrap is not appropriate; comparative forecast verification of continuous variables. In this setting, the question to be answered concerns which of twoweather or climate models is better in the sense of some type of average deviation from observations. The series to be compared are generally strongly dependent, which invalidates the most basic bootstrap technique. This paper also introduces new bootstrap code from the R package distillery that facilitates easy implementation of appropriate methods for paired-difference-of-means bootstrap procedures for dependent data.

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Section: (Vi) Test Inversion Bootstrap CImentioning

confidence: 99%

Section: (Vi) Test Inversion Bootstrap CImentioning

confidence: 99%

Bootstrap Methods for Statistical Inference. Part I: Comparative Forecast Verification for Continuous Variables

Gilleland

2020

Journal of Atmospheric and Oceanic Technology

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“…0 ] ,revd(sum(tt . 0), shape 5 0.2, type 5 "GP", threshold 5 5) a. TIB For the stationary GEV distribution function, Schendel and Thongwichian (2015) performed a simulation test to demonstrate the utility of the TIB approach in comparison to another recommended, nonbootstrap, technique known as the profilelikelihood method. For this special case, they introduced a fast method for employing the TIB, thereby allowing them to perform such a test of the method.…”

Section: Bootstrap Inference For Extreme-value Distribution Functionsmentioning

confidence: 99%

“…Kyselý (2002) found that a parametric bootstrap procedure performed fairly well, but nevertheless had a tendency to yield narrower confidence intervals (CI's) than desired. Schendel andThongwichian (2015, 2017) advocate for the use of the test-inversion bootstrap (TIB; see PI) procedure, but this method can be difficult to implement, especially in the case of nonstationary data.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap Methods for Statistical Inference. Part II: Extreme-Value Analysis

Gilleland

2020

Journal of Atmospheric and Oceanic Technology

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This paper is the sequel to a companion paper on bootstrap resampling that reviews bootstrap methodology for making statistical inferences for atmospheric science applications where the necessary assumptions are often not met for the most commonly used resampling procedures. In particular, this sequel addresses extreme-value analysis applications with discussion on the challenges for finding accurate bootstrap methods in this context. New bootstrap code from the R packages distillery and extRemes is introduced. It is further found that one approach for accurate confidence intervals in this setting is not well suited to the case when the random sample’s distribution is not stationary.

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“…Using the profile likelihood approach, one obtains immediately asymptotic profile likelihood confidence intervals for the shape parameter, which are often assumed to be more accurate than bootstrap confidence intervals (Obeysekera and Salas, 2013;Schendel and Thongwichian, 2015) and those based on the asymptotic normality ofξ (Coles, 2001). Murphy and Van der Vaart (2000) justify the use of the profile likelihood confidence interval for semiparametric models.…”

Section: Simulation Studymentioning

confidence: 99%