machine learning - Are there any contemporary uses of jackknifing ...
WEBBootstrapping does not appear relevant because each data point is a unique location on a map that cannot be re-sampled with replacement. The modeling program itself might be able to ultimately provide what she needs; however, I am interested in general if/when jackknifing can be useful. machine-learning. cross-validation.
r - Bootstrap vs. jackknife - Cross Validated
WEBFeb 20, 2019 · Jackknifing is much older (perhaps ~20 years); it's main advantage in the days when computing power was limited, was that it's computationally much simpler. However, the bootstrap provides information about the whole sampling distribution, and can offer greater precision. The jackknife is still useful in outlier detection, for example in ...
Randomization/permutation tests - Cross Validated
WEBResampling methods are used in (1) estimating precision / accuracy of sample statistics through using subset of data (e.g. Jackknifing) or drawing randomly with replacement from a set of data points (e.g. bootstrapping) (2) Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests ...
cross validation - Jackknife vs. LOOCV - Cross Validated
WEBMar 30, 2015 · In cross-validation you compute a statistic on the left-out sample (s). Most often, you predict the left-out sample (s) by a model built on the kept samples. In jackknifing, you compute a statistic from the kept samples only. Share. Cite. Improve this answer. answered Mar 30, 2015 at 14:31. Tommy L. 1,563 1 12 20.
jackknifing blocks (*not* time series) - Cross Validated
WEBFeb 18, 2015 · 1 Answer. Sorted by: It seems what you are looking for is delete-m (or delete-d) jackknifing, where m = d = 100 m = d = 100. What you are describing is cross validation with 50 groups, and you have made each group the 100 trials. This is a valid, although biased, estimator of your statistic s() s (). Share.
Comparison of the jacknife vs the bootstrap - Cross Validated
WEBHere's the crux: Sampling with replacement from your original sample (i.e. bootstrap) leaves out an average of e−1 ⋅ 100% ≈ 36.7% e − 1 ⋅ 100 % ≈ 36.7 % of your original sample and introduces exact duplicates in the subsamples. In contrast, the jackknife approach only 'costs' you 1 1 observation that is left out in each subsample.
Jackknifing for assessing the "robustness" of test results
WEBFeb 8, 2021 · 2. In a presentation I saw recently, a two-sided t-test was repeated with jackknifed subsets of the original data in order to assess the result's "robustness". In detail, they took a random half of the first group and a random half of the second group (with a couple of hundred samples each), ran a t-test, recorded the result, and repeated this ...
What is the difference between jackknifing and LOOCV?
WEBLeave-one-out cross-validation model technique is very similar to jackknifing resampling, because both omitting each training case and perform retraining of the network on the left-out subset. On wiki page we can read that jackknifing computes a statistic from the kept samples only, while LOOCV computes a statistic on the left-out sample(s).
Estimating the total with jackknife - Cross Validated
WEB2. As I understand it, the jackknife estimator is as follows: ˆθJ = nˆθ − n − 1 n n ∑ i = 1ˆθ − i. Where the ˆθ − i is the estimator with the i'th observation deleted. Thus, even though the ˆθ − i may be less than the "full" calculated estimate, the jackknife estimator still relies on the "full" estimate and just uses the ...
bootstrapping vs. "repeated cross validation"
WEBMar 25, 2015 · It is rightly called " repeated random subsampling (validation)". Sometimes also, "Monte Carlo subsampling" (with fixed subset size). The difference with the related repeated CV method, is simply that it relaxes the disjointness condition on the validation sets. In repeated random subsampling each set drawn is independent of the others.