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.
43
questions
0
votes
0answers
12 views
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 ...
1
vote
1answer
49 views
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 ...
0
votes
0answers
21 views
Prove that Jackknife variance estimate is less than and equal to variance of original estimate
I understand the variance jackknife estimate is equal to the variance of original estimate. but in what circumstance the variance jackknife estimate is less than the variance of original estimate?
14
votes
1answer
2k views
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 ...
4
votes
1answer
114 views
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 ...
0
votes
0answers
53 views
How to average over bootstrap samples of chi square?
I am performing a regression test and I want to see if I am overfitting. When I fit my data $\{y_i | i = 1,2,\ldots\}$ to a model $f(x_i;\vec{\theta})$, I minimize
$$
\chi^2 = \big(y_i-f(x_i;\vec{\...
4
votes
1answer
350 views
How to compute confidence intervals from *weighted* samples?
Imagine we have a webserver, which serves a total of N static URLS.
There are users visiting the URLs every day. At the end of each day, we have data like this:
...
-1
votes
1answer
230 views
Deming regression prediction interval using jackknife resampling
I am trying to write a custom Deming function following the maths in Linnet (1993):
https://www.ncbi.nlm.nih.gov/pubmed/2281234
Using jackknife resampling I calculate the standard error for the ...
0
votes
0answers
20 views
Can one construct “original” data from a function of jackknifed data?
Say I have original, uncorrelated data, $x_i$, with $i = 1,2 \ldots N$. I can jackknife this data set (a simple delete-one)
$$
\bar{x}_{i} = \frac{1}{N-1}\sum_{j \neq i}x_{j} \quad\quad (1)
$$
to ...
1
vote
1answer
93 views
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 ...
0
votes
0answers
53 views
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 ...
0
votes
1answer
62 views
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 ...
1
vote
0answers
28 views
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 ...
0
votes
1answer
58 views
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}...
3
votes
1answer
907 views
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 = \...
0
votes
1answer
647 views
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 ...
1
vote
0answers
315 views
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 ...
0
votes
2answers
537 views
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 ...
1
vote
1answer
142 views
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} ...
5
votes
1answer
170 views
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 ...
5
votes
1answer
942 views
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 $\...
10
votes
0answers
1k views
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 ...
0
votes
1answer
225 views
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) ...
0
votes
0answers
272 views
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 ...
3
votes
1answer
455 views
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 ...
3
votes
2answers
886 views
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 ...
1
vote
0answers
287 views
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 ...
0
votes
0answers
358 views
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....
9
votes
1answer
763 views
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?
20
votes
2answers
9k views
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?
0
votes
1answer
88 views
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 ...
3
votes
2answers
1k views
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....
13
votes
1answer
1k views
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 ...
6
votes
1answer
1k views
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 ...
84
votes
2answers
44k views
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 ...
2
votes
0answers
506 views
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” ...
4
votes
1answer
611 views
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 ...
3
votes
0answers
537 views
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, ...
4
votes
2answers
3k views
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?
...
1
vote
0answers
853 views
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?
1
vote
0answers
312 views
Can we use cross-validation to measure how well a distribution fits sample data?
Let's say I have a data set $X = [1,2,3,4,5]$. And I want to measure how close it is to a Gaussian distribution. Is there a way to use cross-validation to do this?
For example, if I do leave-one-...
6
votes
1answer
965 views
Details regarding the delete-a-group jackknife
I was reading a paper by Phillip S. Kott on DAGJK:
The delete-a-group jackknife. Journal of Official Statistics, 17 (4):521-526. (full text is freely available)
I don't have much of a survey/...
55
votes
1answer
30k views
Bootstrap vs. jackknife
Both bootstrap and jackknife methods can be used to estimate bias and standard error of an estimate and mechanisms of both resampling methods are not huge different: sampling with replacement vs. ...
