Results 81 to 90 of about 719,228 (391)
Skip-sampling: subsampling in the frequency domain [PDF]
arXiv, 2022 Over the last 35 years, several bootstrap methods for time series have been proposed. Popular `time-domain' methods include the block-bootstrap, the stationary bootstrap, the linear process bootstrap, etc.; subsampling for time series is also available, and is closely related to the block-bootstrap.
arxiv
Bootstrap inference when using multiple imputation [PDF]
Statistics in Medicine, 2016 Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric.
M. Schomaker, C. Heumann
semanticscholar +1 more source
, 2016 We work out all of the details required for implementation of the conformal bootstrap program applied to the four-point function of two scalars and two vectors in an abstract conformal field theory in arbitrary dimension.
Rejon-Barrera, Fernando, Robbins, Daniel
core +5 more sources
Advanced Science, EarlyView.MetaTF is a new computational framework that uses single‐cell RNA sequencing (scRNA‐seq) data to infer transcription factor activity with the aim to help in deciphering cells identity starting from a heterogeneous population, and apply it to characterize mouse hematopoietic stem cells and breast cancer epithelial cells, thus identifying a novel subset ...
Yongfei Hu +15 more
wiley +1 more source
MLE with datasets from populations having shared parameters
Statistical Theory and Related Fields, 2023 We consider maximum likelihood estimation with two or more datasets sampled from different populations with shared parameters. Although more datasets with shared parameters can increase statistical accuracy, this paper shows how to handle heterogeneity ...
Jun Shao, Xinyan Wang
doaj +1 more source
The Annals of Statistics, 2015 This paper proposes a numerical bootstrap method that is consistent in many cases where the standard bootstrap is known to fail and where the m-out-of-n bootstrap and subsampling have been the most commonly used inference approaches. We provide asymptotic analysis under both fixed and drifting parameter sequences, and we compare the approximation error
Hong, Han, Li, Jessie
openaire +3 more sources
Alternative approaches to implementing Lagrange multiplier tests for serial correlation in dynamic regression models [PDF]
, 2007 An approximate F-form of the Lagrange multiplier test for serial correlation in dynamic regression models is compared with three bootstrap tests. In one bootstrap procedure, residuals from restricted estimation under the null hypothesis are resampled ...
Godfrey, L.G.
core +1 more source
Advanced Science, EarlyView.Researchers develop advanced tools to study grapevine traits like berry quality and stress resilience. A 200K SNP array and high‐throughput phenotyping enable the identification of loci linked to berry shape, sugar content, acidity, and cold tolerance. Functional validation of genes such as NAC08 reveals roles in cold tolerance.
Yuyu Zhang +11 more
wiley +1 more source
Maximum Entropy Bootstrap for Time Series: The meboot R Package
Journal of Statistical Software, 2008 The maximum entropy bootstrap is an algorithm that creates an ensemble for time series inference. Stationarity is not required and the ensemble satisfies the ergodic theorem and the central limit theorem.
Hrishikesh D. Vinod +1 more
doaj
The Jackknife and the Bootstrap for General Stationary Observations
, 1989 We extend the jackknife and the bootstrap method of estimating standard errors to the case where the observations form a general stationary sequence. We do not attempt a reduction to i.i.d. values.
H. Künsch
semanticscholar +1 more source