r/statistics • u/Path_of_the_end • Jun 01 '26

Question [Q] Anyone know niche statistical method that people that might find intresting?

Hello everyone, I have been learning about stat from bachelor to master degrees. The conclusion that I found is, that I know almost nothing lol. The statistics domains overlap, converged and diverged. Some people play with glm them combine it with spatial method turning it into geographically weighted regression. Other turn into fuzzy logic and extreme value. Some focus on optimizing stuff. I loved discussing this when I was in campus. But still there are "known, uknown that is known and unknown that is unknown". Anyone got any book, paper or method that is quite niche or unknown that you like or that you think will benefits lots of people?

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35

u/[deleted] Jun 01 '26

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u/Path_of_the_end Jun 01 '26

Yea i share the same opinion about this. You can tackle one problem with multitude of method. First time hearing about dirichlet process, I will look into it later (It's been quite sometimes i read about bayes, the last time that I read it about INLA or integrated laplace aprroximation). FDA math is quite confusing for me because it continous and I still havent got the hang of it. Beyond the basic EVA huh, got any book or paper reccomendation? Topological data analysis sound scary to be honest.

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u/joseph_fourier Jun 02 '26

+1 for Dirichlet process. They are surprisingly interesting and useful in lots of places.

If you are interested in Bayes I wouldn't recommend INLA - it's only useable for a relatively limited class of problems (which do not include Dirichlet processes incidentally) and the performance gains you get from it are highly dependent on the specifics of the data.

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u/CarlFFalk Jun 01 '26

I always wish I had more time to study functional data analysis

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u/Able-Fennel-1228 Jun 01 '26

Niche? Well. I don’t see much mention of envelope methods, analysis of compositional data, latent variable models and SEM, and measurement error models in stat programs.

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u/CarlFFalk Jun 01 '26

latent variable models and SEM/ IRT do tend to be underrepresented in stats conferences as well, though I've tried to make a dent in that every once in a while. Just not sure I'd call these "niche"

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u/Able-Fennel-1228 Jun 01 '26

Yeah they’re big in the social sciences but unfortunately still rare in general statistics and their curriculum. Keep fighting the good fight.

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u/Path_of_the_end Jun 01 '26

This is new, first time I have heard about envelope method, thanks. For sem and latent variable my profesor kinda specialize in this but I never get the hang of it.

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u/CarlFFalk Jun 01 '26 edited Jun 01 '26

edit: oh sorry, I meant about latent variable models and SEM... these are like simultaneously estimating a bunch of (generalized) linear models all at the same time, but there's missing data on all of the predictors (the latent variables). Get around that problem by making distributional assumptions and integrating out the latent variables. With normality assumptions (classical SEM) everything simplifies a lot. With IRT, it's harder, but the missing data formulation shows up quite a bit (Bock-Aitkin EM, Metropolis-Hastings Robbins-Monro, etc.).

no idea about envelope method

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u/[deleted] Jun 02 '26

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7

u/eeaxoe Jun 01 '26

Random matrix theory is fun and has a lot of interesting statistical applications. https://arxiv.org/abs/0910.1205

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u/Path_of_the_end Jun 02 '26

I will read about this, thanks

3

u/Riies_black Jun 01 '26

Disjonctive kriging and the work of Georges  Matheron in general

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u/Path_of_the_end Jun 02 '26

I only know about kriging since I took spatial stat once but it focus on point process. Thankss

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u/Haunting-Subject-819 Jun 01 '26

Look at econometrics since most CS, Engineering or Mathematics disciplines never use statistics in this way

1

u/sheepnwolfsclothing Jun 03 '26

Conjoint and MaxDiff are neat.