Questions tagged [jackknife]
The jackknife is one of several resampling methods (see also bootstrap). In the jackknife, one (or sometimes more) points from the data are deleted in each resample.
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Estimating covariance between two random forests
I'm building two random forests in R, one using a "treatment" dataset, $\hat{f}_1$, and the other using a "control" dataset, $\hat{f}_2$, so ...
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Linear relationship seems to depends from one value: would bootstraping help?
Statistics beginner here so please don't shoot if the terminology isn't 100% neat.
In a sample of 45 individuals, I am looking at a relationship between two parameters (Annual_SOM_rate and ...
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Jackknife estimation and testing
I'm using jacknife method to get the uncertainty of my estimation. My estimation restricted to (0,1) and some can be very small. For example, the jackknife estimate for variable A is 0.0000503 and ...
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For small sample sizes, is jackknife superior at controlling Type-I error compared to bootstrap?
This question is motivated by the post here: Can bootstrap be seen as a "cure" for the small sample size?
In the referenced post, we see that the bootstrap approach does not control type-1 ...
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Total least square and jackknife method
I am trying to understand the total least square. I have found in example here, unfortunately it is only in German, but the most important thing are the five example data points. These are (1,3), (3,1)...
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What does the bias of jackknife method mean? [duplicate]
If we apply the jackknife method on a dataset and estimate a bias, what does that mean in physical interpretation? I mean without using statistical words how can I can interpret the results in my own ...
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What does the bias of the jackknife method mean?
If we apply the jackknife method on a dataset and estimate a bias, what does that mean in physical interpretation? I mean without using statistical words how can I can interpret the results?
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What's the "proof" that jackknife is useful for estimating larger population?
What's the "proof" that jackknife is useful for estimating larger population?
That is, since it's proposed as an estimate for a larger population, then why would one use it rather than ...
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Species richness: Why does the more species at one plot imply the more species are missed?
Species richness: Why does the more species at one plot imply the more species are missed?
This is what my lecture notes say.
The more there are species that have been observed only once, the more
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Percentile jackknife?
Percentile bootstrap, where one bootstraps and takes the 2.5th and 97.5th percentiles of the bootstrapped distribution of the statistic of interest are fairly common and well known.
I've never come ...
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How to estimate the effect of sample size on the variance of a statistic (using a given sample)?
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Let's say I have some sample, of size N, with observations ($y_i$ with $i=1,...,N$). And I have some statistic based on the observations (say $S = f(y_1, ..., y_N)$).
I would now like to ...
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What is the best way to estimate an error on stacked data?
I have 400 pixel maps (which are different realisations of the same underlying system) which, individually, don't show much. If I stack them, however, I get a statistical signal; the maps tend to have ...
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Sampling Weights with latent profile analysis package
Is there a package for latent profile analysis in R that can accommodate sampling weights? I am using tidyLPA and would prefer to use this package if anyone has knowledge about how methods such as ...
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Jackknifing for assessing the "robustness" of test results
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 ...
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Why is the jackknife less computationally intensive than the bootstrap?
It's often claimed that the jackknife is less computationally intensive. How is that the case?
My understanding is that the jackknife involves the following steps:
Remove 1 data point
Estimate the ...
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MSE of the Jackknife Estimator for the Uniform distribution
The Jackknife is a resampling method, a predecessor of the Bootstrap, which is useful for estimating the bias and variance of a statistic. This can also be used to apply a "bias correction" to an ...
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How to compute confidence intervals from *weighted* samples?
Imagine we have a webserver, which serves a total of N static URLS.
There are users visiting the URLs every day. At the end of each day, we have data like this:
...
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Deming regression prediction interval using jackknife resampling
I am trying to write a custom Deming function following the maths in Linnet (1993):
https://www.ncbi.nlm.nih.gov/pubmed/2281234
Using jackknife resampling I calculate the standard error for the ...
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Can one construct "original" data from a function of jackknifed data?
Say I have original, uncorrelated data, $x_i$, with $i = 1,2 \ldots N$. I can jackknife this data set (a simple delete-one)
$$
\bar{x}_{i} = \frac{1}{N-1}\sum_{j \neq i}x_{j} \quad\quad (1)
$$
to ...
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How to estimate the confidence interval of the difference of means?
I have two sets of measurements (repeated measurements of two processes, let's call them $X=\{X_i\}_{0...n}$ and $Y=\{Y_i\}_{0...m}$), and I would like to estimate (with confidence interval) the ...
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How can I calculate $\mu_x/\mu_y$ using Jackknife?
I have difficulties in calculating confidence interval using Jackknife.
Suppose that I have two independent samples and I do not know both their population distributions.
I understand how to ...
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Why are jackknife pseudovalues treated as independent?
Treating jackknife pseudovalues as IID variables is ubiquitous among the sources I've come across. However, I never see an attempt to justify it beyond citing a "proposal" by Tukey's 1958 paper, which ...
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What kind of data should be used to demonstrate jack-knife usability?
I tried to use jack-knife method to get the estimation of parameters of the model of the time series. The problem is, that if I use the least squares method, the parameters of a model are always the ...
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jackknife estimator with central limit theorem
Let $\hat{\theta}_n$ be an estimator of the parameter $\theta$ from the sample $\Omega_n$ of $n$ observations, satisfying that $\sqrt{n} (\hat{\theta}_n-\theta) \overset{d}{\longrightarrow} \mathcal{N}...
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What's the point of reporting bootstrap bias?
Suppose the data $X = (X_1,X_2,\cdots,X_n)$ is a vector of iid observations $X_i$ where each $X_i$ has marginal distribution $F(\theta)$. Suppose we observe $x = (x_1,\cdots,x_n)$ and $\hat \theta = \...
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testing for differences using jackknife distributions
I have two distributions relative to two experimental conditions.
I compute a certain index (i.e. coherence) describing each distribution.
I want to see if there is a significant difference ...
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jackknife replicate weights for survey data
I am confused about utilizing the jackknife replicate weights for computing the variance. From the second equation on page 192 of STATA's documentation (http://www.stata.com/manuals13/svy.pdf), the ...
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Estimating the total with jackknife
At a homework assignment, we are supposed to estimate the total in a population using a Horvitz Thompson estimator, and then estimate the variance using the jackknife technique.
As a first step, I ...
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Jackknife and matrix diagonalization
Suppose I have $M$ matrices $n\times n$ of the form:
\begin{equation}
C^{k} = \begin{bmatrix}
x^{k}_{11} & x^{k}_{12} & x^{k}_{13} & \dots & x^{k}_{1n} \\
x^{k}_{21} ...
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What is a good introductory text on resampling methods? [duplicate]
I have found a few decent ones about specific resampling applications such as bootstrapped confidence intervals, but nothing broader. A journal article or book chapter would be preferable to an entire ...
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Why does the jackknife-after-bootstrap estimation of variance give an overestimate?
The jackknife-after-bootstrap method is used to find the an error estimate (for example variance) to a bootstrap estimate. A typical setting is: (1) From a sample of a population, find an estimate $\...
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Comparison of the jacknife vs the bootstrap
I am interested in understanding the relative pros and cons of bootstrap versus jacknife resampling. Both are used in iterative algorithmic approaches to estimating the precision of a prediction or ...
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Tests for comparing correlation values. The winner is?
The question is relatively simple, but trivial.
I have data divided by one fixed effect (Emotion, with 2 conditions: A and B), and I have a covariate, for each of my 20 subjects.
My aim is to
1) ...
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How can bootstrap be used to establish confidence intervals for Theil regression parameters?
In References for methods for calculating the confidence interval for Theil-Sen Estimator, we note that Wilcox (1998, 2009?) proposed the use of bootstrap as a method for finding confidence intervals ...
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Is the Jackknife estimation better than Maximum Likelihood Estimator?
I'm trying to estimate distribution parameters with Maximum Likelihood Estimator (MLE) and Jackknife estimator based on it. The estimation statistic is mean.
Jackknife estimator is considered to be ...
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Statistical analysis of random variable with finite mean and infinite variance
Given a measurement that (we know from theory) has a finite expected value, but infinite variance, is it possible to have some statistical information? Can I get the significance of such data? Can ...
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What sample size is necessary to jackknife estimate variance of large dataset?
So I'm working on a project and I wrote a java library that I can use to perform feature ranking and Naive Bayes classification on data sets. I wrote and tested the library on small data sets so I ...
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Resampling to subset (jackknife) or bootstrap for survival analysis
This is a question regarding survival analysis and bootstrap: Suppose I have a time-to-event dataset with 1000 subjects, and based on this dataset I can obtain some measures or plots (not standard, o....
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Why is backward elimination justified when doing multiple regression?
Does it not result in over-fitting? Would my results be more reliable if I added a jack-knife or bootstrap procedure as a part of the analysis?
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Jackknife vs. LOOCV
Is there really any difference between the jackknife and leave one out cross validation? The procedure seems identical am I missing something?
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jackknifing blocks (*not* time series)
I have the following problem.
There are 50 subjects. Each subject does a block of 100 trials. At the end I want to compute a complicated statistic s() on the data x. Now I want to be able to compute ...
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How to program the deleted d-jack-knife
I have a dataset containing 45 observations. I want to sample times from this dataset, but with sample size equal to 35 each time. So each time I want to delete 10 datapoints from the original dataset....
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Are there any contemporary uses of jackknifing?
The question:
Bootstrapping is superior to jackknifing; however, I am wondering if there are instances where jackknifing is the only or at least a viable option for characterizing uncertainty from ...
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Bootstrap Confidence Intervals for Weir & Cockerham's Fst
I'm working on calculating bootstrap confidence intervals for Weir & Cokerham's Fst.
I want to use the percentile-t method as described in this paper.
I'm calculating the $F_{st}$ value between ...
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Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests
I am trying to understand difference between different resampling methods (Monte Carlo simulation, parametric bootstrapping, non-parametric bootstrapping, jackknifing, cross-validation, randomization ...
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Analysis of a complex sample with replicate weights: error when using survey package
I have a data base from a stratified multi-stage survey. Due to confidentiality reasons, they do not gave me the strata neither the psu, ssu… variables. Instead they gave me the:
“Final weights” ...
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The Bayesian approach to computing estimator bias and variance
From what I understand, jackknife and bootstrapping are frequentist methods for computing statistics (bias, variance, etc.) of an estimator.
Given a sample of my data and an estimator, and assuming ...
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Comparing Gini coefficients: Variance estimation etc. needed?
In a project on software measurement, we plan to use aggregating statistics (e.g., Gini) to describe the concentration of certain observed program attributes (size) among program units (e.g., modules, ...
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How to derive jackknife bias for variance and mean
I am having a hard time to understand how one derives the jackknife bias for the variance and mean.
1) Why do we need an inflation factor of $(n-1)$ when calculating the jackknife bias of the mean?
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Bootstrap or jack-knife for crossvalidation of predictive model?
Is a bootstrap or jack-knife method better for crossvalidation of a multivariate logistic regression based predictive model?
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