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Questions on parametric and non-parametric bootstrap
Accepted answer
16 votes

The answer given by miura is not entirely accurate so I am answering this old question for posterity: (2). These are very different things. The empirical cdf is an estimate of the CDF (distribution) ...

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Benjamini-Hochberg dependency assumptions justified?
3 votes

The validity of the BH procedure depends on the hypothesis tests being positively dependent. If you read their 2001 paper you would see that it is not necessary to be multivariate normal, they gave ...

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Generate distribution based on descriptive statistics
2 votes

You must specify a model. You cannot estimate the model or generate a distribution function given the summary statistics. If you had the data, you could at best do non-parametric estimation, e.g. ...

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Is it necessary to correct alpha in repeated measures ANOVA before any post-hoc comparisons?
2 votes

This is a closed testing procedure, so you must correct the p-values to control type-one error within levels of the hypothesis hierarchy. for example, in a normal ANOVA, you test the global null first ...

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Why does "explaining away" make intuitive sense?
1 votes

I think an easier way to think of it is: If there is any variable $C$ $(0<P(C)<1)$ such that the occurrence of $C$ increases the probability of both $A$ and $B$, then $A$ and $B$ cannot be ...

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Comparing MLE parameters using bootstrapped confidence intervals
1 votes

Bootstrapping is naturally a way of estimating the parameters under the alternative, not under the null. As such it does not immediately lend itself to hypothesis testing. In order to perform ...

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Predictive performance depends more on expertise of data analyst than on method?
1 votes

Actually, I have heard a rumor that decent learning machines are usually better than experts, because the human inclination is to minimize variance at the expense of bias (oversmooth), leading to poor ...

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How to simulate random variables according to the law of a pregiven data sample
1 votes

I would discourage you from using density estimation in such a small data set. In non-parametric density estimation, the bias is on the same order as the variance, which generally is OK if and only if ...

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Conservativeness of tests based on a discrete random variables
1 votes

I have never heard it suggested to use a mid p-value. This will not necessarily control your type-one error. As previously stated, the correct way to achieve a size of .05 is to perform a randomized ...

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Can I use bootstrapping, why or why not?
1 votes

I don't understand your data very well, but I can tell you that an alternative to the multinomial bootstrap that works better for rare events is perturbation / wild bootstrap. Perturbation is ...

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Combining standard errors of fit parameters
Accepted answer
1 votes

If you can't derive Cov(mu, tau) you can bootstrap the statistic of interest, i.e. mu+tau (note that the standard bootstrap may not be valid for non-iid data)

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Time line analysis
0 votes

I would suggest functional data analysis but I suspect you might have a lot of families with too few children to get reasonable estimates. Go ahead and read into it though, as it addresses your needs. ...

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How to analyse a ranking and rating scale together?
0 votes

This is an unsupervised learning task. Here is a very simple idea which if incorrect I hope someone else points out. Feed your ten variables into a PCA to extract 2 PCs. Use the two principal ...

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Significant change in estimate with new observation?
0 votes

$\bar{X}_t = \frac{(t-1)}{t}\bar{X}_{t-1} + \frac{1}{t}X_t$ so I suppose you could say that if $\frac{1}{t}(X_t - \bar{X}_{t-1}) > \epsilon$, then update. You can make similar formulas for more ...

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Do we need a Bonferonni correction when running numerous univariate regression analyses?
0 votes

In order to correctly control your type 1 error you do need to make some sort of adjustment. Are your hypotheses nested in any way? If so you can take advantage of this structure (i.e. closed ...

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