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1answer
23 views

Distribution of inbag matrix when sampling with replacement

Say I take a random sample of size $M$ from a sample of size $N$, like, for example you'd do when bootstrapping in random forest. As you increase $M$, you're more likely to sample any particular ...
1
vote
0answers
30 views

Generating new(fake) probability distributions based on many sample probabilty distributions

I wish to be able to generate new probability distributions that incorporate the characteristics of many sample distributions available to me. I currently have data for the individual daily return ...
2
votes
1answer
38 views

Do both Bootstrap with and without replacement create a distribution?

I'm having a "noisy debate" with colleagues about whether sampling without replacement can still create a distribution. Methodology: A bootstrap (iterative process where I calculate Somers' D for new ...
0
votes
1answer
38 views

How to bootstrap samples from data that has more than dependent variable?

I understand bootstrap sampling with replacement. But what i still not sure about is that how to apply this approach to sample from data that has more than one dependent variables. For example, ...
1
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0answers
40 views

When to use ordinary, balanced, antithetic, or permutation resampling for bootstrap?

I am using boot and would appreciate any explanation as to when using each of these resampling methods would be recommended in practice. I have come across Do who says that: Simulation results ...
0
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0answers
46 views

Generate data with a given covariance matrix and given non-normal distribution

Question I have a dataset of numbers, which I know to be correlated with a covariance matrix that I can reasonably estimate. This correlation has no (known) structure, such as being time, space, or ...
0
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0answers
14 views

Theoretically is it better to sample with or without replacement to approximate a rank test?

Let's say we want to do a rank test since we do not know the exact distribution. Say we have 3 different sample sizes. Which of the following tests (exact permutation, sample with replacement, sample ...
3
votes
1answer
973 views

Sampling from empirical distribution

I have a vector of y (min is > 0, max could be 1), for which, i have no idea what distribution is. But based on the data we have, vector y, we can get the empirical cumulative probability distribution,...
0
votes
0answers
25 views

Almost Sure Convergence and Subsamples

My actual question is in the last paragraph, but I will start with a basic example. In the book "A Course in Large Sample Theory" (Ferguson), they present the Strong Law of Large Numbers as the ...
0
votes
1answer
212 views

Number of bootstrap replicates versus number of simulations

I have some confusion over bootstrap simulation in R. Here is the question: I am asked to use the following parameters to produce simulation: 500 bootstrap replicates 1000 simulations Sample size of ...
1
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0answers
652 views

Two-sample bootstrap hypothesis test

I am a beginner so please be indulgent. I would like to use a two-sample bootstrap hypothesis test for difference of means to this scenario: the impact of a new tool on team members daily output (...
1
vote
1answer
632 views

Using bootstrap to estimate the 95th percentile and confidence interval for skewed data

The problem: I have data of sales per day during a certain period (n=7939). The data is rather skewed (see the first image below). I would like to propose the number of items to resupply every day ...
1
vote
2answers
85 views

When is bootstrapping helpful and used?

When is bootstrapping helpful and when should it be used? I have watched several videos so I understand what bootstrapping does (samples with replacement from a single sample many times and create a ...
0
votes
1answer
48 views

Hypothesis testing on subsamples and cross validation

When working on very large datasets, identifying effects rests more on quantification than significance and many questions/answers give insight on what to do regarding large samples like this (very ...
0
votes
0answers
27 views

Empirical distribution weights [duplicate]

Suppose we have $X_1, X_2,\dots X_n$ from some distribution $F$. We can then form the empirical distribution $F_n$ by saying: $$F_n(x)=\frac{1}{n}\sum_{i=1}^n\mathbb{I}_{\{X_n\leq x\}}$$ I find it ...
2
votes
0answers
98 views

Bootstrap Resampling Vs KDE Resampling

Let $\xi\in\mathbb{R}^{m}$ be a random vector with joint desity function $f$, and let $\widehat{\xi}_{1},\ldots,\widehat{\xi}_{N}$ be a sample of $\xi$. We have that the kernel density estimator (...
0
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0answers
32 views

Making predictions based on the data of 1 'sample'

Let's assume that we have a dataset $D$ consisting of $n$ samples/instances $x_i$ and a particular value of $Y$. The objective is to predict the quantity $Y$ for some arbitrary dataset. We know that ...
13
votes
3answers
4k views

Why is bootstrapping useful?

If all you are doing is re-sampling from the empirical distribution, why not just study the empirical distribution? For example instead of studying the variability by repeated sampling, why not just ...
0
votes
1answer
28 views

Validate 10% sample from population

I have two databases which include amount of passengers between two routes. One is the full dataset, while the other is supposedly a 10% sample. So for example, the full database will display a route ...
1
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0answers
54 views

Estimating covariance from single sample

We have a sample distribution $p(x)$ and the iid sample $\{x_1,\dots,x_n\}$. I have the two following random variables, calculated from $\{x_1,\dots,x_n\}$: $$A = \dfrac{1}{n}\sum_{i=1}^{n}f(x_i)$$ $$...
1
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0answers
63 views

Bootstrap-based comparison of gene values in two independent experiments

I have a matrix of N gene-based observations (between 0 and 1) from 2 experiments like the following (The actual matrix is much bigger). \begin{array} {|r|r|r|} \...
0
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0answers
787 views

Sampling without replacement

In the context of statistical inference and machine learning, bootstrapping is used to indicate sampling with replacement (e.g. in the context of Random Forest or calculating population standard ...
4
votes
0answers
144 views

When will bootstrapping struggle to provide an accurate distribution?

Can someone give an example of a statistic for which bootstrapping might struggle to provide an accurate sampling distribution?
9
votes
1answer
1k views

Chance that bootstrap sample is exactly the same as the original sample

Just want to check some reasoning. If my original sample is of size $n$ and I bootstrap it, then my thought process is as follows: $\frac{1}{n}$ is the chance of any observation drawn from the ...
0
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0answers
20 views

Approximate likelihood of covariance matrix by bootstrapping? [duplicate]

Say I have some data $X \in \mathbb{R}^{M\times N}$, such that $X_i \sim Normal(\mu, \Sigma)$. It's easy to compute the likelihood of $\Sigma$ conditioned on $X$ and $\mu$: $$ L(\Sigma)=p(X|\mu, \...
1
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0answers
31 views

Low represented data in dataset

I am trying to create a regression model (SVM) for certain data. The dataset consists of few subsets being a representation of different elements. However literature says they can be combined into one ...
9
votes
2answers
5k views

Size of bootstrap samples

I'm learning about bootstrapping as a means of estimating the variance of a sample statistic. I have one basic doubt. Quoting from http://web.stanford.edu/class/psych252/tutorials/doBootstrapPrimer....
7
votes
1answer
513 views

Bootstrapping a sample from a finite population

Can someone point me to some reference for theory on bootstrapping a sample took from a population of known size? I am used to use Bootstrap to calculate confidence intervals of a sample when the ...
2
votes
1answer
800 views

Statistical test for random sample of data

I'm trying to determine if some particular measurements - in this case taken from a subset of genes of interest (50 genes) - show a significant difference to the rest of the population (15000 genes), ...
2
votes
0answers
58 views

Does a bootstrap distribution of the mean statistic give us uncertainity of our overall sampling procedure?

Say that we bootstrapped the mean statistic, and we get a wide bootstrap distribution. Does this give us intuition on the issues with sampling? That is, if we have a wide bootstrap distribution, does ...
0
votes
2answers
47 views

Variance calculation [duplicate]

Can someone explain why: Given a set of n independent observations Z1...Zn, each with variance K. The variance of the mean is K/n?
0
votes
1answer
149 views

Appropriate model for feature subset selection

I am working with a feature selection problem. What I am trying to do is find optimal subset of features for classification. My data consist of 100 features and 300 instances, and class label is ...
21
votes
1answer
6k views

Bootstrapping vs Bayesian Bootstrapping conceptually?

I'm having a trouble understanding what a Bayesian Bootstrapping process is, and how that would differ from your normal bootstrapping. And if someone could offer an intuitive/conceptual review and ...
1
vote
0answers
101 views

Quick question on confidence interval under bootstrap - what is N?

I am trying to understand what N represents when calculating confidence intervals under bootstrap? In this scenario, I have a sample with 188 values, so the sample size, SS, is 188. I am interested ...
3
votes
1answer
111 views

How often will sampling distribution of the mean not be normally distributed?

Kabacoff 2015 suggests that if we're not willing to assume the sampling distribution of the mean is normally distributed, we should use bootstrapping to estimate the sampling distribution of the mean. ...
8
votes
2answers
3k views

Simulate from Kernel Density Estimate (empirical PDF)

I have a vector X of N=900 observations that are best modeled by a global bandwidth Kernel density estimator (parametric models, ...
2
votes
2answers
72 views

Distribution of p(x) in empirical model

I am having a hard time to exactly name what I am looking for (I am quite sure it already exists out there...) so I'll start with a concrete example: I have a population of discrete colours (red, ...
0
votes
1answer
98 views

Bootstrap and MonteCarlo Method

I am trying to make sense of the bootstrap method. I am studying on Rice, "mathematical statistics and data analysis" Here it is its explanation of the bootstrap method: Imagine for the moment ...
1
vote
2answers
175 views

Bootstrap resampling for constructing hypothesis test

I need to use bootstrap resampling to test the significant difference between two datasets (data1 & data2). I have already used bootstrap resampling to estimate the confidence interval of the mean ...
1
vote
0answers
200 views

Can you perform bootstrap resampling from a sampling distribution?

The quick and to-the-point question I have is: Can you perform bootstrap resampling on a sampling distribution, using the sampling distribution as if it were an original sample of observations? What ...
13
votes
2answers
2k views

Best suggested textbooks on Bootstrap resampling?

I just wanted to ask which are in your opinion the best available books on bootstrap out there. By this I don't necessarily only mean the one written by its developers. Could you please indicate ...
2
votes
1answer
68 views

How to interpret if my sample statistic is way out in the tail of the bootstrap distribution

I use bootstrapping to generate the distribution / histogram of my sample statistic and find out that the value of my real sample statistic is way out in the tail. What does this mean? Does it mean ...
1
vote
0answers
121 views

Using bootstrapping simulation to compare estimators in survey

I would like to estimate the mean of a population and select a best estimator with minimum variance of the estimated mean. Suppose that I have two estimators est1 and est2, and they could refer to any ...
8
votes
1answer
722 views

Can I subsample a large dataset at every MCMC iteration?

Problem: I want to perform a Gibbs sampling to infer some posterior over a large dataset. Unfortunatelly, my model is not very simple and thus sampling is too slow. I would consider variational or ...
2
votes
1answer
354 views

Logistic regression and discrepant sample sizes between 0/1 groups

I currently working in a multivariate logistic model but I have a problem regarding the sample size of my observations: -The "success" (1) event group has a sample size of 249 distinct observations - ...
3
votes
1answer
842 views

Addressing Non-response in a Convenience Sample

I am studying customer satisfaction in a large hierarchical organization. I plan to administer a voluntary survey to customers across the organization, and need to address non-response in my analysis. ...
2
votes
1answer
277 views

Multiple samples vs. bootstrapping

I'm somewhat new to understanding statistics meaningfully, and the bootstrap in particular. Let's say you're doing something (polling, running experiment, whatever), and can afford to get 1000 ...
4
votes
0answers
1k views

Should the mean of the bootstrapped distribution always be asymptotically equal to the sample estimate?

Suppose I bootstrap the distribution of the sample mean. Normally, one would use the mean of the bootstrapped distribution as point estimate of the parameter and the s.d. as its standard error. The ...
0
votes
1answer
393 views

Advantages of bootstrap aggregating (sampling)?

I am testing the robustness of a predictive model that I have previously established. I used to withdraw multiple random samples to see how robust the model is. Few days ago I came across bootstrap ...
-1
votes
1answer
660 views

How do I randomize a larger population, from an existing population in R?

I have a small 20k (176 variables) test dataset. I want to create a random bigger dataset for testing, using the rows (data) and columns (names) from the small dataset. Currently I have: ...