Results 31 to 40 of about 22,191,063 (377)
Bootstrap for neural model selection [PDF]
, 2002 Bootstrap techniques (also called resampling computation techniques) have introduced new advances in modeling and model evaluation. Using resampling methods to construct a series of new samples which are based on the original data set, allows to estimate
Cottrell, Marie +2 more
core +3 more sources
Bootstrap confidence sets under model misspecification [PDF]
, 2014 A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and a possible model misspecification. Theoretical results justify the bootstrap validity for a small or moderate sample size and allow
Spokoiny, Vladimir, Zhilova, Mayya
core +3 more sources
UFBoot2: Improving the Ultrafast Bootstrap Approximation
bioRxiv, 2017 The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased ...
D. T. Hoang +4 more
semanticscholar +1 more source
Stats, 2022 The bootstrap method is often used for variance estimation in sample surveys with a stratified multistage sampling design. It is typically implemented by producing a set of bootstrap weights that is made available to users and that accounts for the ...
Jean-François Beaumont, Nelson Émond
doaj +1 more source
Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving
Risks, 2023 This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors.
Greg Taylor, Gráinne McGuire
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Finite-size effects for anisotropic bootstrap percolation: logarithmic corrections [PDF]
, 2007 In this note we analyze an anisotropic, two-dimensional bootstrap percolation model introduced by Gravner and Griffeath. We present upper and lower bounds on the finite-size effects.
A. Holroyd +28 more
core +4 more sources
Bootstrapping INAR models [PDF]
Bernoulli, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jentsch, Carsten, Weiß, Christian H.
openaire +4 more sources
Bootstrapping 2d ϕ 4 theory with Hamiltonian truncation data
Journal of High Energy Physics, 2022 We combine the methods of Hamiltonian Truncation and the recently proposed generalisation of the S-matrix bootstrap that includes local operators to determine the two-particle scattering amplitude and the two-particle form factor of the stress tensor at ...
Hongbin Chen +2 more
doaj +1 more source
Bootstrapping the Kronig-Penney model
Physical Review D, 2022 21 pages, 5 figures, typos corrected, version to appear in Physical Review ...
Matthew J. Blacker +2 more
openaire +2 more sources
Robust model selection using the out-of-bag bootstrap in linear regression
Scientific Reports, 2022 Outlying observations have a large influence on the linear model selection process. In this article, we present a novel approach to robust model selection in linear regression to accommodate the situations where outliers are present in the data.
Fazli Rabbi +5 more
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