The bootstrap is a resampling method to estimate the sampling distribution of a statistic.
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Which bootstrapped regression model should I choose?
I have a binary logistic regression model with a DV (disease: yes/no) and 5 predictors (demographics [age, gender, tobacco smoking (yes/no)], a medical index (ordinal) and one random treatment ...
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2answers
63 views
Understanding bootstrap method for confidence interval of correlation coefficients
Please correct me where I'm wrong:
My understanding of bootstrapping is that it is a way to estimate the distribution of some statistic (mean, standard error, Pearson's correlation coeff, etc), ...
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20 views
Assessing Significance in Neural Networks
I'm trying to determine statistical significance of inputs on the dependent variable using neural networks. I'm trying to test the null hypothesis that the partial derivatives are equal to zero. I ...
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1answer
45 views
Bootstrapping to find confidence intervals. Very small sample size
I am trying to generate confidence intervals for estimates of numbers of fish observed with a very small number of surveys. Most of the numbers of fish observed are very small as well.
The estimate ...
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0answers
15 views
What are guidelines for SmartPLS boostrapping case size?
In SmartPLS, bootstrapping is used to generate the t statistic from which statistical significance can be judged. The two main bootstrapping parameters are case and sample size. Increasing the sample ...
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32 views
Block bootstrap for dependent data with unequal sampling intervals?
I have data from a natural archive (lake sediment). For various reasons it is usually impossible to sample the archive equally in time, and we end up with a time series where essentially we have ...
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1answer
110 views
Bootstrapping power estimates for a bootstrap test
Assume I want to use a (nonparametric) bootstrap test for a hypothesis with a sample size of $n_1$ and I already have $n_0$ actual samples on which to base my power estimates. Usually, we would also ...
2
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0answers
42 views
Bootstrapping clusters in R
I am running a negative binomial regression of clinic counts in each county in the entire country (~3k counties). I'd like to at least partially account for the non-independence of neighboring ...
2
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1answer
149 views
Bootstrapping importance sampling estimates
I've got two algorithms, $A$ and $B$, I want to evaluate. Both algorithms differ by the distribution of points they produce. Let us suppose that $A$ generates points from $P_S$ distribution while B ...
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15 views
Implementing regression slope coefficient comparison using bootstrap in R [migrated]
In this CrossValidated Answer, Gung mentioned about how to compare the slope coefficient of regression models using bootstrap.
He talked about 6 steps to do it.
I wrote the R code for step 1 to 4 but ...
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1answer
57 views
Is it appropriate to use boot strapping to measure variance?
So, I always thought the idea of bootstrapping was that you have a sample from which you obtain an estimator for some function of the population (like the average height). And then when you boot strap ...
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37 views
Performance decay when testing on bootstrap data
I have a strategy that has a Sharpe ratio of 1.6 when back tested over the past 10 years. When I run this same strategy on re-sampled data, the performance of the strategy goes down to 1.32.
Should ...
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39 views
Computing a bootstrap confidence interval for the prediction error with the percentile and the BCa method
I have two related questions regarding the computation of a non-parametric bootstrap confidence interval for the prediction error.
Setting: I have a sample S from a data population P and a learner L, ...
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1answer
73 views
confidence interval for classification error---binomial assumption vs. bootsrap resampling
I am developing a classifier using a set of N patterns, where N~1000. I am using K-fold cross-validation (with K=5) and computing the probability of classification error p (typical value is p=0.03). ...
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1answer
185 views
Is bootstrapping standard errors and confidence intervals appropriate in regressions where homoscedasticity assumption is violated?
If in standard OLS regressions two assumptions are violated (normal distribution of errors, homoscedasticity), is bootstrapping standard errors and confidence intervals an appropriate alternative to ...
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23 views
Compare observed distribution of values vs bootstrapped values
To prove the efficacy of a test (to be more precise, a moderated t-test from two groups of samples), I need to compare the p-values obtained from my test against a null distribution of 1000 random ...
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37 views
For bootstrapping, why does a higher subsample size lead to lower variance?
I've been working on a bootstrapping problem that's left me a little confused and wondering whether I'm doing things correctly.
We have around 200 samples from a population of about 3,400, we want to ...
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1answer
51 views
Confidence bounds for PDF
I build confidence bounds for estimating PDF of the empirical sample using bootstrapping:
...
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1answer
50 views
Random sample using KDE or bootstrapping
I have an experimental sample, size of about 1000 values. I need to generate a much larger sample for simulation. I can create a samples like this:
...
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1answer
68 views
Bootstrapped confidence intervals for the parameters of a linear model applied to multiply imputed data
I would like to construct CIs for $\beta$ in the linear model
$Y = X\beta + \epsilon$
I observe $\{X', Y'\}$ which is $\{X,Y\}$ contaminated with values missing at random. $\epsilon$ is not Gaussian ...
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0answers
21 views
confidence interval with small supopulations
I am currently using a large dataset (n=1.850) composed of smaller samples of several countries. I am currently aiming to describe the sample and infer to the population using simple frequencies ...
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3answers
67 views
Population with known distribution (not normal) versus small sample
I have a complete population consisting of 6587 individuals (genes) and for each individual I have a distance value. These distance values are not normally distributed.
I'm interested in determining ...
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83 views
Kolmogorov-Smirnov and bootstrap
The KS test uses the statistic
$$
D_n=\sup_x |\hat{F}_n(x)-F_0(x)|
$$
where $F_0(x)$ is the distribution to be tested and $\hat{F}_n(x)$ the empirical distribution. Under the null hypothesis $D_n$ is ...
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36 views
Are bootstrapped parameter standard errors unbiased in time series data?
I'm analyzing multivariate panel data which includes autocorrelated time series variables. The response variable is binary and I'm considering a random intercept to account for between subject ...
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54 views
Out of sample bootstrapping and significance
I am investigating the relationship between temperature fields obtained from numerical weather models and electricity demand. I am applying a PCA-based approach, i.e. I study the linear relationship ...
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33 views
computing a p-value for bootstrapped-mean difference [duplicate]
I have read a number of times about computing p-values in bootstrapping.
Unfortunately, I could not use none of them.
Maybe you can help me, please?
I obtained 10000 bootstrap resamples of the ...
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48 views
How do you think about the central estimate when the confidence interval is asymmetric?
I'm using a wild bootstrap to create confidence intervals around fitted values of the following model, for a specific combination of the factors, as x varies across its range.
...
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103 views
Why does the mean of the bootstrapped distribution not equal the original summary stat?
I have n samples and their average. There's some correlation so I used a moving block bootstrap to get an empirical distribution of the mean. The mean of this empirical bootstrapped distribution seems ...
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53 views
Bootstrap Prediction Intervals
My question concerns the construction of forecast prediction intervals using bootstrapping.
I have a 36 month time series, which I am using to perform point forecasts for the next 12 months using ...
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1answer
73 views
Compute the variance of parameter estimates given limited number of samples
I'd like to infer the variance of estimated parameter $\hat\theta$ of the density function of $f(x;\theta)$ given only a limited number of samples $X_1,\cdots,X_n$.
Bootstrapping doesn't perform well ...
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1answer
89 views
Bootstrap USL/LSL for non-normal data
This is new statistics space for me, so please excuse my ignorance.
I have some data (N=180) that isn't normally distributed (verified in Minitab, P<0.005) and I want to use the data to get a +/- ...
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0answers
23 views
p-value variation in spectral analysis
I am using the R function specdens from the package bootspecdens to compare spectral densities for two series. For the two series, the p-value was 0.321, then 0.372, and finally 0.346. I know that the ...
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1answer
216 views
how to bootstrap p-value in ttest in Stata?
Suppose I need to run ttest bhar12=0 and the output comes as:
...
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105 views
Bootstrapping Methodology
I have this bootstrap setting:
Given $n$ vector-valued statistics (each statistic is a vector) of dimension $r \times 1$, I generated $1000$ bootstrap samples and then generated an estimate of each ...
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1answer
108 views
Building the dataset for Random Forest training procedure
I should use the bagging (bootstrap aggregating) technique in order to train a random forest classifier. I read here the description of this learning technique, but I have not figured out how I ...
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43 views
Why do lmp (from lmPerm package) and boot results vary widely
Using built in states data set I fit
lm1<-lm(Murder~Illiteracy*Income),
lm2<-lmp(Murder~Illiteracy*Income),
results<-boot(...,formula=Murder~Illiteracy*Income)
lm1 and result coefficients ...
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1answer
63 views
Comparing MLE parameters using bootstrapped confidence intervals
I have MLE curve parameter estimates for 3 populations (2 per population), and am looking for a clever way to compare them.
At the moment, I am non-parametrically bootstrapping my datasets in order to ...
2
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1answer
207 views
How do I calculate prediction intervals for random forest predictions?
In regression problems random forests yield a prediction for each case by averaging the results of each single tree in the forest. I would like to draw a 95% prediction interval around each ...
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2answers
148 views
Manually computing bootstrap standard errors in the linear regression setting
I have written an R script for obtaining bootstrapped standard errors in the linear regression setting.
In practice, first in a model building step I select the final model to be applied at each ...
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217 views
Variance Covariance matrix of regression coefficients
In a procedure, I am resampling (bootstrapping) from a Y vector and X matrix.
For each resample, I standardize.
Then I run a linear model, and obtain the regression coefficients for each variable of ...
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0answers
43 views
Testing Differences In Dinucleotide Frequencies Between Two Sets Of DNA Sequences
I have two sets of DNA sequences, and have calculated the frequency that particular dinucleotides (e.g. AT, GC, AA etc.) appear at each site along the sequence for each set. This has generated traces ...
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38 views
Calculate standard deviation of data within each bar of a histogram [duplicate]
I have data with which I plot a histogram.
I would like to know the standard deviation of each bar of histogram.
Do you think it is an acceptable method to divide the samples into sub-samples for ...
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0answers
118 views
Defining this simulation approach (Bootstrap, Monte Carlo)
I am currently carrying out simulations based on two different longitudinal (multi-state) models. In practice, these two models are aimed at parametrically estimating the transition probabilities ...
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1answer
70 views
Gamma and binomial semi-parametric bootstrap [closed]
I am performing a semi parametric bootstrap in R on a Gamma Distributed data and a Binomial distributed data. The main challenge am facing is the fact that the residual variance depends on the mean ...
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1answer
114 views
How to use OLS or NLS on very small sample size?
right now I am facing the problem of handling a very small sample size which consists of 8 observations with 24 variables.
I would like to do first just simple OLS with one dependent and 23 ...
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2answers
144 views
Questions on paramatric and non-parametric bootstrap
I am reading the chapter on Frequent Statistics from Kevin Murphy's book "Machine Learning - A Probabilistic Perspective". The section on bootstrap reads:
The bootstrap is a simple Monte Carlo ...
2
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1answer
119 views
Bootstrapping unbalanced clustered data (non-parametric bootstrap)
I am trying to figure out how to simulate bootstrap samples from a dataset with unbalanced clusters. The approach I would like to adopt is non-parametric pairs bootstrap, which easily allows to ...
3
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2answers
315 views
Bootstrapping hierarchical/multilevel data (resampling clusters)
I am producing a script for creating bootstrap samples from the cats dataset (from the -MASS- package).
Following the Davidson ...
2
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1answer
162 views
Bootstrapping - with population data (dependent variable is not normally distributed)
Thank you in advance for help! I am conducting a study using General Linear Modeling on the distribution of financial aid at a college. I am not looking to project my findings onto a larger ...
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1answer
136 views
Dealing with a small sample size
Suppose we have a data set consists of, say, 5 or 10 observations. The only thing we know about this set is that it came from a positive right skewed distribution. Now suppose we want to fit a ...
