Does any living statistician come close to the level of Donald Rubin in terms of research impact? Missing data analysis, causal inference, EM algorithm, any probably more. He just walks around creating new subfields.
Statisticians use a technique that leverages randomness to deal with the unknown
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Re: Statisticians use a technique that leverages randomness to deal with the unknown
#12Earlier quoted context omitted.
Maybe in academia, where sketchy incentives rule. In industry, p-hacking is great till you’re eventually caught for doing nonsense that isn’t driving real impact (still, the lead time is enough to mint money).
Very doubtful. There are plenty of drugs that get approved and are of questionable value. Plenty of procedures that turn out to be not useful. The incentives in industry are even worse because everything depends on lying with data if you can do it.
I assume by "in industry" they meant in jobs where you are doing data analysis to support decisions that your employer is making. This would be any typical "data scientist" job nowadays. There the consequences of BSing are felt by the entity that pays you, and will eventually come back around to you.
The incentives in medicine are more similar to those in academia, where your job is to cook up data that convinces someone else of your results, with highly imbalanced incentives that reward fraud.
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#13I don’t know. I find quanta articles very high noise. It’s always hyping something
I agree with that. I skip the Quanta magazine articles, mainly because the titles seem to be a little to hyped for my taste and don't represent the content as well as they should.
Yes, typically a short conversation with an LLM gives me more info and understanding of a topic than reading a Quanta article.
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#14Does any living statistician come close to the level of Donald Rubin in terms of research impact? Missing data analysis, causal inference, EM algorithm, any probably more. He just walks around creating new subfields.
Efron?
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#15why not use regression on the existing entries to infer what the missing ones should be?
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#16I wish they actually engaged with this issue instead of writing a fluff piece. There are plenty of problems with multiple imputation. Not the least of which is that it's far too easy to do the equivalent of p hacking and get your data to be significant by playing games with how you do the imputation. Garbage in, garbage out. I think all of these methods should be abolished from the curriculum entirely. When I review…
Clearly you know your stuff. Are there any not-super-technical references where an argument against using imputation is clearly explained?
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#17Not one mention of the EM algorithm, which is, as far as I can understand, is being described here (https://en.m.wikipedia.org/wiki/Expectation%E2%80%93maximiza...). It has so many applications, among which is estimating number of clusters for a Gaussian mixture model.
An ELI5 intro: https://abidlabs.github.io/EM-Algorithm/
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#18I wish they actually engaged with this issue instead of writing a fluff piece. There are plenty of problems with multiple imputation. Not the least of which is that it's far too easy to do the equivalent of p hacking and get your data to be significant by playing games with how you do the imputation. Garbage in, garbage out. I think all of these methods should be abolished from the curriculum entirely. When I review…
I feel like multiple imputation is fine when you have data missing at random.
The problem is that data is never actually missing at random and there’s always some sort of interesting variable that confounds which pieces are missing
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#19why not use regression on the existing entries to infer what the missing ones should be?
That would push things towards the mean... not necessarily a bad thing, but presumably later steps of the analysis will be pooling/averaging data together so not that useful.
A more interesting approach, let's call it OPTION2, would be to sample from the predictive distribution of a regression (regression mean + noise), which would result in more variability in the imputations, although random so might not what you want.
The multiple imputation approach seems to be a resampling methods of obtaining OPTION2, w/o need to assume linear regression model.
Re: Statisticians use a technique that leverages randomness to deal with the unknown
#20Not one mention of the EM algorithm, which is, as far as I can understand, is being described here ( https://en.m.wikipedia.org/wiki/Expectation%E2%80%93maximiza... ). It has so many applications, among which is estimating number of clusters for a Gaussian mixture model. An ELI5 intro: https://abidlabs.github.io/EM-Algorithm/
It does not appear to be what's being described here? Could you perhaps expand on the equivalence between the two if it is?