resampling and regression
A new method to analyse the pace of child development: Cox regression validated by a bootstrap resampling procedure
... Cox regression model, enabling to focus on differences related to the speed by which distinct capabilities are ...Cox regression model is a critical ...Cox regression analysis or selection procedures ...
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An empirical study using permutation-based resampling in meta-regression
... We also found that the P values obtained using per- mutation tests are more conservative, or larger, than P values obtained using standard meta-regression meth- ods. Specifically, the P values for significant ...
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Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems
... neurons. Thus, the LARS algorithm can be used to select an optimum subset of the candidate neurons which minimizes (7) for a given value of λ, selected using cross-validation. One must note that this is not a novel ...
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An Approximate Analytical Approach to Resampling Averages
... of resampling aver- ...approximate resampling averages by solving a set of nonlinear equations rather than by explicit ...for regression with Gaussian processes (GP) (which is a kernel method that ...
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Perturbation Bootstrap in Regression.
... We conclude this section with a brief literature review. The perturbation bootstrap was introduced by Jin, Ying, and Wei (2001) as a resampling procedure where the objective function has a U-process structure. ...
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An approximate analytical approach to resampling averages
... approximate resampling averages by solving a set of nonlinear equations rather than by explicit ...for regression with Gaussian processes (GP) (which is a kernel method that has gained high popularity in ...
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A nonparametric hypothesis test via the Bootstrap resampling
... The present study aims at adapting Fan and Li’s (1996) nonparametric asymp- totic hypothesis test procedure to the bootstrap framework as this framework, as shown by Hall and Horowitz (1996), promises smaller ...
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Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator
... perturbation resampling is still applicable because it only depends on the first order derivative ∇`(θ; ...robust regression via ψ-type M-estimator where the loss function may be not ...
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Issues of Class Imbalance in Classification of Binary Data: A Review
... some resampling techniques were applied using ‘ROSE’, ‘DMwR’ and ‘caret’ packages in R [16, 13, 17] to improve the model sensitivity and precision which were initially ...
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Best Parameter Interval for Ridge Estimates by Resampling Method
... Ridge Regression, as proposed by Hoerl and Kennard [1], is one of the popular, albeit controversial, estimation procedures for combating ...the regression coefficients have ...
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Partially linear models
... linear regression smoothing in ...linear regression techniques to various statistical ...squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, ...
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Fingerprint resampling: A generic method for efficient resampling
... A first problem that needs to be solved is that some estimation problems call for a constrained opti- mization. If a parameter is constrained, this can lead to a non-smooth fingerprint-optimum relation. As an ...
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PubMedCentral-PMC5186805.pdf
... spatial resampling is often applied to achieve rigid-body motion ...spatial resampling could also produce spurious variance, and lead to unexpected errors on the amplitude of BOLD ...spatial ...
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MapReduce particle filtering with exact resampling and deterministic runtime
... the resampling algorithm, they all consist of two core stages: calculating how many copies of each sample to generate and generating that number of copies of each ...variance resampling (also known as ...
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High Speed DAC with Resampling Architecture
... distributed resampling which uses one switch for one current source all are driven by common ...The resampling is done before currents are applied to R-2R ladder network so that each resampling ...
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Resampling Detection in Digital Images: A Survey
... as resampling operation which changes certain characteristics of the pasted ...Thus resampling is the default fingerprint present in most of the forged image and resampling detection became a ...
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Improving the Identification of Phenotypic Abnormalities and Sexual Dimorphism in Mice When Studying Rare Event Categorical Characteristics
... The sim- ulation was conducted for various genotype effect sizes (0, Figure 1 Resampling studies to assess the control of type-one errors. Resampling studies of control data to mirror a [r] ...
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Accelerated Failure Time Model for Correlated Survival Data: Efficient Estimation and Inference.
... Cox regression model for the marginal hazard functions of time to recurrent events [55], and the marginal models for the mean/rate functions of the counting process for recurrent events [27, 28, 32, 46] are well ...
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Quality evaluation of a selected partition: An approach based on resampling methods
... The aim of this work on cluster analysis is to provide a methodology to analyse and assess the quality of a selected partition (the best partition according to several validation indexes). In the proposed approach, the ...
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Sieve bootstrap t-tests on long-run average parameters
... two resampling algorithms that build both on the …xed regressor bootstrap of Hansen (2000) and on the restricted residuals approach of Nankervis and Savin ...
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