Matched-Block Bootstrap for Dependent Data

Carlstein, Edward; Do, Kim‐Anh; Hall, Peter; Hesterberg, Tim; Künsch, Hans R.

doi:10.2307/3318719

Cited by 90 publications

(73 citation statements)

References 17 publications

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“…This is a general problem for the block bootstrap and has been dealt with in a number of ways; either by trying to match blocks at their ends (Carlstein et al, 1998) or by down-weighting the block ends (Paparoditis and Politis, 2001). In this paper we propose an alternative based on transforming theˆ * i (t) to have the same covariance structure as was assumed for the original process.…”

Section: Bootstrap Proceduresmentioning

confidence: 99%

Functional convolution models

Asencio

Hooker

Gao

2014

Statistical Modelling

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This paper considers the application of functional data analysis methods to modeling particulate matter trajectories from dynamometer experiments. In particular the functional convolution model is introduced as a restriction of the functional historical linear model that allows for functional data of different lengths to be used. We present a penalized ordinary least squares estimator for the model and a novel bootstrap procedure to provide pointwise confidence regions for the estimated convolution functions. The model is illustrated on the California E55/59 study of diesel truck emissions.

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Section: Bootstrap Proceduresmentioning

confidence: 99%

Functional convolution models

Asencio

Hooker

Gao

2014

Statistical Modelling

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“…The moving block bootstrap method samples time series 'blocks' or sets of fixed length l of consecutive data values and preserves much of the correlation structure in the blocks (Wilks, 1997). The matched block bootstrap technique is the same as the moving block method, except that the original dependence structure is maintained within the blocks as well as at block boundaries by applying a 'rank matching' procedure to join blocks that were a priori more likely to be close to one another (Carlstein et al, 1998;Srinivas and Srinivasan, 2005). The block lengths used here were estimated following Politis and White (2004), and from a publicly available Matlab computer code (http://fmg.lse.ac.uk/∼patton/code.html).…”

Section: Atmospheric Circulation and Sstmentioning

confidence: 99%

“…99-102;Davison and Hinkley, 1997, pp. 396-401), and 'matched block' bootstrap (Carlstein et al, 1998;Srinivas and Srinivasan, 2005) tests, the latter two of which can even be applied to weakly non-stationary time series (Carlstein et al, 1998).…”

Section: Atmospheric Circulation and Sstmentioning

confidence: 99%

Large‐scale atmospheric circulation and global sea surface temperature associations with Horn of Africa June–September rainfall

Segele

Lamb

Leslie

2008

Intl Journal of Climatology

145

171

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This study uses correlation, regression, and composite analyses for the period 1970-1999 to explore the relationships between the June-September rainfall in the Horn of Africa (especially Ethiopian) and large-scale regional atmospheric circulation patterns across Africa and the Atlantic and Indian Oceans, and global sea surface temperature (SST) anomalies. Abundant rainfall in the Horn of Africa is associated with enhanced westerlies across western and central Africa. These westerlies are produced by a stronger north-east directed mean sea level pressure (MSLP) gradient resulting from MSLP intensification over the Gulf of Guinea and deepening of the monsoon trough across the Arabian Peninsula. This is reflected by a strong correlation (−0.71) between 5-day (pentad) Ethiopian rainfall and the Gulf of Guinea minus the Arabian Peninsula MSLP difference. This correlation decreases to −0.39 when the seasonal cycles are removed from both time series. A wet Horn of Africa monsoon is also associated with deep moist air extending up to mid-troposphere and large water vapour transport convergence across much of Ethiopia, a strong Somali low-level jet, and a strong tropical easterly jet (TEJ). Although there are large changes in TEJ strength, the position of the jet axis shows little variation between wet and dry events. Associated with the TEJ, the strongest upper level divergence occurs at 100 hPa, where the raw/de-seasonalized zonal wind speed correlates negatively (−0.71/−0.23) with the corresponding Ethiopian rainfall at the pentad time-scale. Furthermore, SSTs over the equatorial Pacific, Indian, and southern Atlantic Oceans correlate strongly with contemporary Ethiopian summer rainfall. In general, Ethiopian rainfall is suppressed during El Niño and enhanced during La Niña. This identification and documentation of the regional atmospheric circulation patterns and global SST anomalies directly linked to rainfall variability over Ethiopia/Horn of Africa, are crucial for developing statistical prediction schemes and designing climate model simulations for the region on intra-seasonal to inter-annual time-scales.

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“…However, the standard bootstrap assumes that the observations in the data are independent and identically distributed [7]. The moving block bootstrap method extends the standard bootstrap method in include correlated observations [15]. To maintain the temporal dependence in data, sequential observations are randomly sampled in blocks, rather than single observations.…”

Section: Moving Block Bootstrap Methodsmentioning

confidence: 99%

Wind power prediction interval estimation method using wavelet-transform neuro-fuzzy network

Cai

Zhang

2015

IFS

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Abstract. Wind power point forecasting is the primary method to deal with its uncertainty. However, in many applications, the probabilistic interval of wind power is more useful than traditional point forecasting. Methods to determine the probabilistic interval of wind power point forecasting value is very essential to power system operations. Based on the bootstrap method, this paper proposed a wavelet transform combined with a neuro-fuzzy network model to estimate the prediction interval of wind power. In the model, to account for the ramp event of wind power series, a wavelet-based ramp event was used and the moving block bootstrap method, which considers the dependence of wind power series, was used to construct sampling datasets. Then, the bootstrapped datasets were estimated by a neuro-fuzzy network inference system. A case study provided a 90% confidence level of prediction intervals, which was constructed to examine the effectiveness of the model.

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Matched-Block Bootstrap for Dependent Data

Cited by 90 publications

References 17 publications

Functional convolution models

Functional convolution models

Large‐scale atmospheric circulation and global sea surface temperature associations with Horn of Africa June–September rainfall

Wind power prediction interval estimation method using wavelet-transform neuro-fuzzy network

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