The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence

By Edward Carlstein. 1986

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Citations in the corpus

Trend analysis of climate time series: A review of methods (2019, background): “An optimal block length selector (Carlstein, 1986; Sherman et al., 1998) is = × ×l NINT a a n{[6 /(1 )] },opt 1/2 2 2/3 1/3 (A.5) where NINT is the nearest integer function.”
A Smoothed Least Squares Estimator for Threshold Regression Models (2007, methodology): “In the time series case, one generally has to use a more complicated resampling method like the block bootstrap (Carlstein (1986)) and Künsch (1989)) to capture the e¤ect of the dependence structure on the limiting distribution.”
Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise (2004, methodology): “Interestingly Politis, Roman& Wolf (1999) noted that the subsample estimator (of the long-run variance) of Carlstein (1986) is identical to both the moving block bootstrap estimator and the Jackknife estimator in this case, seeKünsch (1989) and Liu & Singh (1992).”
Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the measurement time scale (2008, unknown): “Phys., 9, 497–513, 2009 www.atmos-chem-phys.net/9/497/2009/ (Carlstein, 1986; Kunsch, 1989; Liu and Singh, 1992).”
Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients (2021, background): “Carlstein [24], aware that time series do not satisfy the i.”
Are there Monday effects in stock returns: a stochastic dominance approach (2007, methodology): “Instead of this overlapping block bootstrap, we can also use the non-overlapping block bootstrap of Carlstein (1986) or the stationary bootstrap of Politis and Romano (1994), see Lahiri (1999) for a recent survey.”
The Impact of Bootstrap Methods on Time Series Analysis (2003, background): “…estimator for anθ̂n is sought, it can be constructed by the sample variance of the normalized subsample values abθ̂i,b for i = 1, . . . , q; consistency of the subsampling estimator of variance for general statistics was shown by Carlstein (1986) under some uniform integrability conditions.”
A note on composite likelihood inference and model selection (2005, methodology): “…Section 3.1, we shall analyze discrete values time series data using the pairwise likelihood and J(θ) is suitably estimated by means of a reuse sampling procedure, called window subsampling (Carlstein 1986, Hall & Jing 1996, Garcia-Soidan & Hall 1997, Heagerty & Lele 1998, Lumley & Heagerty 1999).”
Bootstrapping GMM estimators for time series (2003, methodology): “We use the block bootstrap originally proposed by Künsch (1989) for weakly dependent data (see also Carlstein, 1986).”
Subsampling Realised Kernels (2007, background): “For instan e, the sparsely sampled realised varian e, 10(XÆ); isidenti al to Carlstein's subsample estimator (of the varian e of a sample mean when the mean iszero), see Carlstein (1986).”
Dynamic connectivity regression: Determining state-related changes in brain connectivity (2012, methodology): “While the ordinary nonparametric bootstrap assumes the data are uncorrelated and resamples the entire data set, the block bootstrap introduced by Carlstein (1986), assumes the data are stationary.”
Sequential block bootstrap in a Hilbert space with application to change point analysis (2014, methodology): “To circumvent this problem we will use the nonoverlapping block bootstrap of Carlstein (1986) to construct a process with the same limiting distribution as 1√ n ∑ nt i=1 (Xi − μ).”
Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification (2006, methodology): “…between the block bootstrap and the stationary bootstrap of Politis and Romano (PR: 1994a) is that the former uses a deterministic block length, which may be either overlapping as in Künsch (1989) or non-overlapping as in Carlstein (1986), while the latter resamples using blocks of random length.”
Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach (2012, unknown): “The size of the ith subwindow |I (i)n | can be determined following Carlstein (1986) as∣∣I (i)n ∣∣ = ( 2β1 − β2 )2/3 (3n 2 )1/3 .”
Local likelihood smoothing of sample extremes (2000, background): “Block bootstrap schemes (Carlstein, 1986; KuÈ nsch, 1989) made little di erence to the applications reported in this paper, though they might be useful in other contexts.”