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Statisticians use a technique that leverages randomness to deal with the unknown

quantamagazine.org

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Re: Statisticians use a technique that leverages randomness to deal with the unknown

#21

Post by miki123211 »

Jun8 wrote: post #17

Not 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 has so many applications, among which is estimating number of clusters for a Gaussian mixture model

Any sources for that? As far as I remember, EM is used to calculate actual cluster parameters (means, covariances etc), but I'm not aware of any usage to estimate what number of clusters works best.

Source: I've implemented EM for GMMs for a college assignment once, but I'm a bit hazy on the details.

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#22

Post by TaurenHunter »

Donald Rubin is kind of a modern day Leibniz...

Rubin Causal Model

Propensity Score Matching

Contributions to

Bayesian Inference

Missing data mechanisms

Survey sampling

Causal inference in observations

Multiple comparisons and hypothesis testing

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#23

Post by underbiding »

parpfish wrote: post #18
light_hue_1 wrote: post #3

I 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

True true but how do you account for missing data based on variables you care about and those you don't?

More specifically, how do you determine if the pattern you seem to be identifying is actually related to the phenomenon being measured and not an error in the measurement tools themselves?

For example, a significant pattern of answers to "Yes / No: have you ever been assaulted?" are blank. This could be (A), respondents who were assaulted are more likely to leave it blank out of shame or (B) someone handling the spreadsheet accidentally dropped some rows in the data (because lets be serious here, its all spreadsheets and emails...).

While you could say that (B) should be theoretically "more truly random", we can't assume that there isn't a pattern to the way those rows were dropped (i.e. a pattern imposed on some algorithm that bugged out and dropped those rows).

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#25

Post by aabaker99 »

light_hue_1 wrote: post #3

I 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…

My intuition would be that there are certain conditions under which Bayesian inference for the missing data and multiple imputation lead to the same results.

What is the distinction?

The scenario described in the paper could be represented in a Bayesian method or not. “For a given missing value in one copy, randomly assign a guess from your distribution.” Here “my distribution” could be Bayesian or not but either way it’s still up to the statistician to make good choices about the model. The Bayesian can p hack here all the same.

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#26

Post by aquafox »

clircle wrote: post #11

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.

Andrew Gelman?

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#27

Post by billfruit »

jll29 wrote: post #5
xiaodai wrote: post #2

I don’t know. I find quanta articles very high noise. It’s always hyping something

I don't find the language of the article full of "hype"; they describe the history of different forms of imputation from single to multiple to ML-based. The table is particularly useful as it describes what the article is all about in a way that can stick to students' minds. I'm very grateful for QuantaMagazine for its popular science reporting.

The Quanta articles usually have a gossipy style and are very low information density.

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#28

Post by stdbrouw »

ivansavz wrote: post #19
paulpauper wrote: post #15

why 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 w…

Multiple imputation simply means you impute multiple times and run the analysis on each complete (imputed) dataset so you can incorporate the uncertainty that comes from guessing at missing values into your final confidence intervals and such. How you actually do the imputation will depend on the type of variable, the amount of missingness etc. A draw from the predictive distribution of a linear model of other variables without missing data is definitely a common method, but in a state-of-the-art multiple imputation package like mi in R you can choose from dozens.

Re: Statisticians use a technique that leverages randomness to deal with the unknown

#30

Post by richrichie »

selimthegrim wrote: post #14
clircle wrote: post #11

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.

Efron?

& Tibshirani
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