Window Subsampling of Estimating Functions with Application to Regression Models

By Patrick J. Heagerty, Thomas Lumley. 2000

132 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/469c70e425baabd86edda4bc52dc32e0072e558b

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, methodology): “But a windows resampling procedure (see Heagerty and Lumley, 2000) may be used to estimate J .”
Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach (2012, background): “gif"/>, Heagerty and Lumley ( 2000 ) proposed as an estimator of <inline-graphic xmlns:xlink="http://www.”
A Composite Likelihood Approach in Fitting Spatial Point Process Models (2006, methodology): “As an alternative, we can use subsampling to estimate (θ0) (see Heagerty and Lumley 2000; Politis and Sherman 2001 for details).”
A Nonparametric Test for Spatial Isotropy Using Subsampling (2004, methodology): “Heagerty and Lumley (2000) used a subsampling method to estimate the covariance matrix of estimators derived from estimating functions.”
A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, methodology): “18 windows re-sampling procedure of Heagerty and Lumley (2000) to estimate ) (θ J .”
Spatial autocorrelation and statistical tests in ecology (2002, methodology): “Another application of randomization procedures is to derive variance estimators using jackknifing estimation, as described by Lele (1991) and more recently by Heagerty and Lumley (2000).”
A note on composite likelihood inference and model selection (2005, background): “Heagerty & Lumley (2000) show that the optimal dimension m for the subwindows is Cn1/3, where C is a constant, with suggested values between 4 and 8, which depends on the strength of the dependence within the data.”
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, methodology): “But, since the spatial dependence fades with distance, we can use a windows resampling procedure (see Heagerty and Lumley, 2000) to estimate )(θJ .”
Joint composite estimating functions in spatiotemporal models (2012, methodology): “Nott and Rydén (1999) and Kuk and Nott (2000) are among those researchers who have advocated the incorporation of correlations between composite pairs into the estimation.”