Jackknife Resampling Puns

I am currently working on a linear regression model (panel heteroscedasticity-robust standard errors). It was suggested to me to use delete-1 Jackknife Resampling in order to check whether my results are robust. I have not worked with this method before (rather CooksD or leverage vs. residuals plots) and I am not sure how to interpret results of the resampling.

I was suggested the follwing: Interpret each individual p-value for the respective coefficient. If the coefficient is statistically significant in the baseline model, but not for one or several of the resampled models ... find something new to work on. This seems wrong. Shouldnt I be looking at the ranges or averages for each coefficient respectively? Or maybe t-values? Is delete-1 even suitable for panel models? Wouldn't it be more appropriate to delete countries (several observations over time) instead of observations?

If you use machine learning in statistics, you usually want to know how accurate an estimate obtained from some sample is, its confidence interval in technical terms. In the 1980'ies, Bradley Efron invented the bootstrap, a generic way to do compute confidence intervals with computers and pseudo-random numbers that works for any estimator applied to any distribution, from the lowly median to your trained convolutional neural net. Bootstrapping tools have been available in the R language for a long time, but with the resample module you can now do bootstrapping and jackknifing in Python with efficient unit-tested pure Python implementations that require only scipy and numpy.

Features

• Bootstrap samples (ordinary or balanced, both with optional stratification) of arrays with arbitrary dimension
• Parametric bootstrap samples (Gaussian, Poisson, gamma, etc.) of one-dimensional arrays
• Bootstrap confidence intervals (percentile or BCa)
• Jackknife estimates of bias and variance
• Randomization-based variants of traditional statistical tests (t-test, ANOVA F-test, K-S test, etc.)
• Tools for working with empirical distributions (cumulative distribution, quantile, and influence functions)

PyPI GitHub Docs

Hello, everyone. I am trying to help a friend with his thesis. I’m not expert in statistics, but I know a bit and I’m quite familiar with programming (Python) and math in general. Me and my friend are basically trying to test the hypothesis that soil treatment influences its insect fauna. During his research, 72 samples were taken, then all the insects within each soil sample were identified (by subfamily). There are 6 different possible soil treatments. The data looks something like this:

There are way more than 4 subfamilies of insects in the real table, by the way. But, after all the data was collected, insects with very low occurrence were all labeled together as “Other”. His teacher advisor told him he should do a jack knife resampling due to the small total sample size of the experiment. For the resampling, I considered the individual soil sample as the unit of observation. After the resampling was done, I grouped the data by soil treatment and summed so the jackknife resample ended up something like this:

I ran a chi-square test for each one of the jack knife resamples, then stored the p values on a list and then checked the confidence interval of the list for 95% confidence. The p-value was less than 0.05 so I concluded that null hypothesis could be rejected. Before running the chi-square test I transposed the table, but it appears that that has no effect on the results. We also wanted to know which insect subfamilies appeared to be dependent on soil treatment. So, I reran the test for each insect subfamily with all the other subfamilies being labeled as “Other”. The results seemed consistent, but I am not confident on many of the steps. Some of the questions I have are as follow:

• Does it make sense to use the soil sample as a unit of observation for the jack knife resample?

• Does it make sense to group by soil treatment before doing the chi-square test?

• Does it make sense to pair the p value analysis with a confidence interval analysis?

Thanks in advance and sorry for the long post. I tried to make it as short as possible, but I afraid it ended up long

I’m a statistics Major at my university, and I really enjoyed my statistical inference class I took last semester. We had a big emphasis on estimators, hypothesis testing, and some computation. we learned how to derive estimators, maximum likelihood estimation, rao-Blackwell, sufficiency, bias, variance, compute MSE, consistency , compute confidence intervals, bootstrapping, different parametric tests, Monte Carlo, Also did all of this in R. Upcoming and later statistical inference classes in my major include more of a computational side, coding up simulations, resampling methods, jackknife, permutation tests, non parametric hypothesis testing etc.

I have one statistical learning class, which is an elective. My question is, with such a rigorous emphasis on statistical inference and hypothesis testing, how much can I apply the stuff I’ve learned here in industry and in data science? Can the concepts I listed above be applied anywhere in Machine learning? The linear models and glms class i take will cover most of the basics of statistical learning. But the classical hypothesis testing and statistical inference, where is that applied in today’s world of data science and machine learning?

I don't want to step on anybody's toes here, but the amount of non-dad jokes here in this subreddit really annoys me. First of all, dad jokes CAN be NSFW, it clearly says so in the sub rules. Secondly, it doesn't automatically make it a dad joke if it's from a conversation between you and your child. Most importantly, the jokes that your CHILDREN tell YOU are not dad jokes. The point of a dad joke is that it's so cheesy only a dad who's trying to be funny would make such a joke. That's it. They are stupid plays on words, lame puns and so on. There has to be a clever pun or wordplay for it to be considered a dad joke.

Again, to all the fellow dads, I apologise if I'm sounding too harsh. But I just needed to get it off my chest.

The nurse asked the rabbit, “what is your blood type?”

“I am probably a type O” said the rabbit.

Mentos

(I will see myself out)

The doctor says it terminal.

Alot of great jokes get posted here! However just because you have a joke, doesn't mean it's a dad joke.

THIS IS NOT ABOUT NSFW, THIS IS ABOUT LONG JOKES, BLONDE JOKES, SEXUAL JOKES, KNOCK KNOCK JOKES, POLITICAL JOKES, ETC BEING POSTED IN A DAD JOKE SUB

Try telling these sexual jokes that get posted here, to your kid and see how your spouse likes it.. if that goes well, Try telling one of your friends kid about your sex life being like Coca cola, first it was normal, than light and now zero , and see if the parents are OK with you telling their kid the "dad joke"

I'm not even referencing the NSFW, I'm saying Dad jokes are corny, and sometimes painful, not sexual

So check out r/jokes for all types of jokes

r/unclejokes for dirty jokes

r/3amjokes for real weird and alot of OC

r/cleandadjokes If your really sick of seeing not dad jokes in r/dadjokes

Punchline !

Edit: this is not a post about NSFW , This is about jokes, knock knock jokes, blonde jokes, political jokes etc being posted in a dad joke sub

Edit 2: don't touch the thermostat

Hi all,

i was wondering if any of you know a book like The Jackknife and Bootstrap, in the sense of mathematical rigurousity, for resampling techniques, but that is a bit more current?

This book is great, but 2 things I dont like. The notation is a bit cumberson for my taste, but more importantly is lacking in other resampling techniques, like permutation test.

Also, since is more than 20 years old, theres probably some outdated content there.

Any help would be cool, thanks

Do your worst!

How the hell am I suppose to know when it’s raining in Sweden?

Mathematical puns makes me number

We told her she can lean on us for support. Although, we are going to have to change her driver's license, her height is going down by a foot. I don't want to go too far out on a limb here but it better not be a hack job.

Ants don’t even have the concept fathers, let alone a good dad joke. Keep r/ants out of my r/dadjokes.

But no, seriously. I understand rule 7 is great to have intelligent discussion, but sometimes it feels like 1 in 10 posts here is someone getting upset about the jokes on this sub. Let the mods deal with it, they regulate the sub.

They were cooked in Greece.

I'm surprised it hasn't decade.

He lost May

Now that I listen to albums, I hardly ever leave the house.

Said if she ever hosts a gender reveal party, when it comes time to pop the balloon she'll spray everyone with water.

Gender is fluid.

Two muffins are in an oven, one muffin looks at the other and says "is it just me, or is it hot in here?"

Then the other muffin says "AHH, TALKING MUFFIN!!!"

Don't you know a good pun is its own reword?

But let me give it a shot.

For context I'm a Refuse Driver (Garbage man) & today I was on food waste. After I'd tipped I was checking the wagon for any defects when I spotted a lone pea balanced on the lifts.

I said "hey look, an escaPEA"

No one near me but it didn't half make me laugh for a good hour or so!

Edit: I can't believe how much this has blown up. Thank you everyone I've had a blast reading through the replies 😂

It really does, I swear!

He’s the new temp.

And now I’m cannelloni

Because she wanted to see the task manager.

But that’s comparing apples to oranges

And boy are my arms legs.

Amy

Put it on my bill

Heard they've been doing some shady business.

but then I remembered it was ground this morning.

Edit: Thank you guys for the awards, they're much nicer than the cardboard sleeve I've been using and reassures me that my jokes aren't stale

Edit 2: I have already been made aware that Men In Black 3 has told a version of this joke before. If the joke is not new to you, please enjoy any of the single origin puns in the comments

They’re on standbi

BamBOO!

A play on words.

Calcium, nickel, neon

If you do statistics, you usually want to know how accurate an estimate obtained from some sample is, its confidence interval in technical terms. Computing that is not easy and it usually involves a problem-specific recipe.

In the 1980'ies, Bradley Efron invented the bootstrap, a generic way to do compute confidence intervals with computers and pseudo-random numbers that works for any estimator applied to any distribution, from the lowly median to your trained convolutional neural net. Bootstrapping tools have been available in the R language for a long time, but with the resample module you can now do bootstrapping and jackknifing in Python with efficient unit-tested pure Python implementations that require only scipy and numpy. Enjoy and leave a star if you like it!

Features

• Bootstrap samples (ordinary or balanced, both with optional stratification) of arrays with arbitrary dimension
• Parametric bootstrap samples (Gaussian, Poisson, gamma, etc.) of one-dimensional arrays
• Bootstrap confidence intervals (percentile or BCa)
• Jackknife estimates of bias and variance
• Randomization-based variants of traditional statistical tests (t-test, ANOVA F-test, K-S test, etc.)
• Tools for working with empirical distributions (cumulative distribution, quantile, and influence functions)

PyPI GitHub Docs