Browsing by Subject "Confidence intervals"
Now showing items 1-15 of 15
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Bootstrap prediction intervals for linear, nonlinear and nonparametric autoregressions
(2016)In order to construct prediction intervals without the cumbersome-and typically unjustifiable-assumption of Gaussianity, some form of resampling is necessary. The regression set-up has been well-studied in the literature ...
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Bootstrap prediction intervals for Markov processes
(2014)Given time series data X1,…,Xn, the problem of optimal prediction of Xn+1 has been well-studied. The same is not true, however, as regards the problem of constructing a prediction interval with prespecified coverage ...
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Direct bound on the total decay width of the top quark in pp- collisions at s=1.96□□TeV
(2009)We present the first direct experimental bound on the total decay width of the top quark, t, using 955□□pb-1 of the Tevatrona's pp- collisions recorded by the Collider Detector at Fermilab. We identify 253 top-antitop pair ...
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Dose-dense sequential chemotherapy with epirubicin and paclitaxel in advanced breast cancer
(2001)The purpose of this study was to evaluate the activity and toxicity profile of dose-dense sequential chemotherapy with epirubicin (EPI) and paclitaxel in advanced breast cancer (ABC). From January to September 1997, 41 ...
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Generalized seasonal tapered block bootstrap
(2016)In this paper a new block bootstrap method for periodic time series called Generalized Seasonal Tapered Block Bootstrap (GSTBB) is introduced. Consistency of the GSTBB for parameters associated with periodically correlated ...
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Generalized seasonal tapered block bootstrapAAA
(2016)In this paper a new block bootstrap method for periodic time series called Generalized Seasonal Tapered Block Bootstrap (GSTBB) is introduced. Consistency of the GSTBB for parameters associated with periodically correlated ...
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The Impact of Bootstrap Methods on Time Series Analysis
(2003)Sparked by Efron's seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data - mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed ...
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Large-sample inference in the general AR(1) model
(2000)The situation where the available data arise from a general AR(1) model is discussed, and two new avenues for constructing confidence intervals for the unknown autoregressive root are proposed, one based on a Central Limit ...
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Large-sample inference in the general AR(1) modelAAA
(2000)The situation where the available data arise from a general AR(1) model is discussed, and two new avenues for constructing confidence intervals for the unknown autoregressive root are proposed, one based on a Central Limit ...
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The local bootstrap for kernel estimators under general dependence conditions
(2000)We consider the problem of estimating the distribution of a nonparametric (kernel) estimator of the conditional expectation g(Greek cursive chi
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The local bootstrap for kernel estimators under general dependence conditionsAAA
(2000)We consider the problem of estimating the distribution of a nonparametric (kernel) estimator of the conditional expectation g(Greek cursive chi
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Moment estimation for statistics from marked point processes
(2001)In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations
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On the asymptotic theory of subsampling
(2001)A general approach to constructing confidence intervals by subsampling was presented in Politis and Romano (1994). The crux of the method is recomputing a statistic over subsamples of the data, and these recomputed values ...
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Subsampling confidence intervals for parameters of atmospheric time series: Block size choice and calibration
(2005)Problems of practical implementation of the computer intensive subsampling methodology are addressed by Monte Carlo simulations of a situation typical for atmospheric time series. The motivating data were collected under ...
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Subsampling Inference with K Populations and a Non-standard Behrens-Fisher Problem
(2012)We revisit the methodology and historical development of subsampling, and then explore in detail its use in hypothesis testing, an area which has received surprisingly modest attention. In particular, the general set-up ...