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Predictive Modeling with Supervised Machine Learning
Published in Altuna Akalin, Computational Genomics with R, 2020
In some cases, we might have too few data points and it might be too costly to set aside a significant portion of the data set as a holdout test set. In these cases a resampling-based technique such as cross-validation may be useful.
On Evaluation of Climate Models
Published in Vyacheslav Lyubchich, Yulia R. Gel, K. Halimeda Kilbourne, Thomas J. Miller, Nathaniel K. Newlands, Adam B. Smith, Evaluating Climate Change Impacts, 2020
Kaibo Gong, Snigdhansu Chatterjee
More serious challenges are tied to the theoretical framework assumptions and methodological steps needed to construct an appropriate test statistic, and to get the distribution of the test statistic under the null and alternative hypotheses. For test statistics, Braverman et al (2017) and Chatterjee (2019) consider functions of the vector of wavelet coefficients from the coarse levels, while Gong et al (2018) reconstruct the climate signal using such coarse-level wavelets and construct a statistic from the amplitude of the oscillations. Resampling has been the main tool to obtain the test statistic distribution in Braverman et al (2017) and Chatterjee (2019), while Gong et al (2018) used a more traditional testing approach. We now present a brief description of the resampling-based approaches used in Braverman et al (2017) and Chatterjee (2019).
Machine learning for radiation oncology
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Multiple resampling usually includes the bootstrap, permutation test, and repeated k-fold CV. The basic idea of bootstrapping is to randomly draw data sets with replacement from the training data, and each sample set has the same size as the original training set. The bootstrap is a general tool for assessing statistical accuracy. Bootstrapping is the practice of estimating properties of an estimator by measuring those properties when sampling from an approximating distribution. It is often used as a robust alternative to inference based on parametric assumptions when those assumptions are in doubt or where parametric inference is impossible or requires very complicated formulas for the calculation of standard errors. Bootstrapping is useful in practice when the sample is too small for CV approaches to yield a good estimate. [32]
The electrophysiological masking level difference: effects of age and mediation of hearing and cognition
Published in International Journal of Audiology, 2023
Lauren K. Dillard, Amy L. Cochran, Cynthia G. Fowler
Mediation analysis facilitated the evaluation of the a priori conceptual framework outlined in this article, which focussed on the effect of age and mediation of age-related hearing loss and/or global cognitive function. The relationships among variables (i.e. potential collinearity) are not a weakness of this study, but rather are inherent to the study design and analytic approach. This conceptual framework assumes the mediators (age-related hearing loss, global cognitive function) operate through a pathway of ageing. Historically, authors recommended relationships among mediator and outcome variables be evaluated prior to conducting analysis in order to “establish mediation” (Judd and Kenny 1981; Baron and Kenny 1986). However, more recent articles focus on benefits of the bias-corrected bootstrap (i.e. resampling tests; as used in this study), which can be combined with theory-based modelling, in order to enhance statistical power (Fritz and MacKinnon 2007). This study also aimed to promote mediation-based modelling in the field of audiology and hearing science (Andersson and Westin 2008).
Borderline SARS-CoV-2 patients: the trace behind
Published in Expert Review of Molecular Diagnostics, 2023
Bojana Mohar Vitezić, Elena Štefančić, Davorka Repac Antić, Tanja Grubić Kezele, Maja Abram, Marina Bubonja-Šonje
According to the manufacturer’s recommendation, transcription and SARS-CoV-2 detection were performed with AccuPower® SARS-CoV-2 Multiplex Real-Time RT-PCR Kit (Bioneer, South Korea). The kit detects three SARS-CoV-2 gene targets: E gene, RdRp, and N gene (detection of E gene/FAM, RdRp, and N gene/JOE), and IPC/Cyanine 5. Real-time PCR was performed on Exicycler™ 96 RT-PCR instrument (Bioneer, South Korea) with automated result analysis by Analysis ExiDiagnosis V4 software. Analysis software has implemented an algorithm for result analysis: the sample is considered positive if both E and RdRp-N targets amplify or only RdRp-N with Ct-cutoff values ≤35. The sample is considered ‘presumptive positive’ if just the E gene target amplifies. Results were claimed as inconclusive for those results with no detected E gene or amplified E gene with Ct above 36 and amplification curves of RdRp-N with Ct between 36 and 40. Our laboratory manages the inconclusive PCR results with a request for resampling within 3 days, and all second results are from a second sample of the same individual. So differences between patient status (converted to positive or negative) are based on the same extraction and detection method but performed on the second sample taken after the inconclusive PCR result. The number of resampling samples varied among hospital departments, but generally, we received the requested resamples within 1–5 days.
Efficient accounting for estimation uncertainty in coherent forecasting of count processes
Published in Journal of Applied Statistics, 2022
Christian H. Weiß, Annika Homburg, Layth C. Alwan, Gabriel Frahm, Rainer Göb
Inspired by the proposals of [5,12], we suggest and investigate another approach to account for the estimation uncertainty if doing coherent forecasting. Using a resampling technique, we directly approximate the distribution of the coherent forecast value (which is random due to the randomness of the sample 2. Its performance is investigated in Section 3, where results from a comprehensive simulation study are discussed. The real-data example presented in Section 4 illustrates the application of the proposed resampling approach in practice. In particular, it is shown that the presentation of the full ensemble of forecast values need not be done in a tabular form (i.e. as a frequency table of the resampled forecasts), but visual solutions are also possible. The article concludes in Section 5.