Resampling Methods for Dependent Data

By Soumendra Nath Lahiri. 2003

516 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering

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Citations in the corpus

Trend analysis of climate time series: A review of methods (2019, background): “, slowly), the MBB may have to be replaced by a subsampling procedure to yield reliable uncertainty measures (Lahiri, 2003).”
Inference on Causal and Structural Parameters using Many Moment Inequalities (2013, background): “Ω,A,P). We follow the basic notation introduced in Section 3. For the sake of simplicity, assume that there exists a constant Dn ≥ 1 such that |Xij −µj| ≤ Dn a.s. for 1 ≤ i≤ n,1 ≤ j≤ p. 23We refer to Lahiri (2003) as a general reference on resampling methods for dependent data. MANY MOMENT INEQUALIT”
Ridge Regression and Extensions for Genomewide Selection in Maize (2009, background): “Optimality of cross-validation methods often rests on independence assumptions, and generalizing to the case of dependent data is not straightforward (Lahiri, 2003).”
On the detection of changes in autoregressive time series, II. Resampling procedures (2008, unknown): “, Lahiri [20], Franke et al.”
Analysis of parameter uncertainty in semi-distributed hydrological models using bootstrap method: a case study of SWAT model applied to Yingluoxia watershed in northwest China. (2010, unknown): “859] 0.00 [0.691, 0.703] 0.00 v__SMTMP.bsn [2.145, 3.022] 0.00 [2.462, 2.496] 0.00 v__ALPHA_BF.gw [0.345, 0.427] 0.32 [0.391, 0.494] 0.00 v__SMFMX.bsn [4.449, 5.490] 0.10 [3.459, 3.497] 0.00 v__SURLAG.bsn [4.04, 5.88] 0.00 [4.091, 4.416] 0.00 80 Z. Li et al. / Journal of Hydrology 385 (2010) 76–83”
A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods ☆ (2007, methodology): “A book length treatment of various issues involving resampling methods for dependent data is given in Lahiri (2003).”
Testing for high-dimensional white noise using maximum cross-correlations (2016, methodology): “e with dependent data. There exist various estimation methods for long-run covariances, including the kernel-type estimators (Andrews, 1991), and the estimators utilizing the moving block bootstraps (Lahiri, 2003). See also Den Haan & Levin (1997) and Kiefer et al. (2000). We adopt the kernel-ty”
The Term Structure of Growth-at-Risk (2018, unknown): “To account for temporal dependence present in the data, we use a block-bootstrap strategy (Lahiri, 2003; and Kapetanios, 2008).”
Robust Performance Hypothesis Testing with the Sharpe Ratio (2008, background): “The problem is that a nonstudentized bootstrap does not provide improved inference accuracy compared to ‘standard’ inference based on asymptotic normality; again see Hall (1992) and Lahiri (2003). Scherer (2004) addresses this problem for i.”
A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data (2013, methodology): “For dependent data, the bootstrap method has been extensively studied under the expanding-domain asymptotics (e.g., Hall 1985; Davison and Hinkley 1997; Lahiri 2003).”
Hypothesis Testing in Econometrics (2010, methodology): “are dependent over time, one must use a suitable time-series bootstrap; e.g., see Lahiri (2003) .”
Break function regression (2009, methodology): “Lahiri [ 26 ] explains the “traditional” method to generate a number of samples that is very much larger than n at 56 The European Physical Journal Special Topics”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “The same time blocks are used for all the regions to preserve spatial correlation and CBB formally leads to consistent confidence intervals of spatial correlations (Lahiri, 2003).”