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For whatever reason, statistics seems to be in the midst of a serious branding problem. Maybe traditional frameworks of inference are too conservative for non-r
by thearn4 10y ago
For whatever reason, statistics seems to be in the midst of a serious branding problem. Maybe traditional frameworks of inference are too conservative for non-researcher's tastes?
I'm curious what Fisher, Neyman, and Pearson would say about the current state of the field. Especially considering how often they and other statisticians disagreed with each other throughout the 20th century.
- estro 10y agoI believe your branding hypothesis is correct, and all of this hype around neural nets and machine learning is creating a startup ecosystem ripe for market correction. I've heard a million times that most machine learning problems are solvable by regression, and I expect to hear that a million times more.
- CuriouslyC 10y agoRegression is great if your output is a linear function of your inputs. Neural networks don't have that limitation. For a long time people have assumed that linear functions are the rule and nonlinearity is the exception, but the fact that nonlinear predictors are trouncing linear ones in many domains seems to be proving that false. As for deep neural networks, in my opinion they are just an ensemble of neural networks, and ensemble methods have been shown to produce best-in-class results almost regardless of the base learning algorithm.
- fullshark 10y agoThe only real advantage linear modeling has is marginal effects can be determined easily. This matters for some business intelligence / decision making applications but if you want to build a model with as small errors as possible there's really no reason to restrict your model to be linear in the age of massive computing power.
- digitalzombie 10y agoWell it's also easy to model. NN needs lots of data from what I've read. Also the whole sport analytic prediction industry mostly uses linear regression... But next hype thing is medical data for them but they're super slow in adopting new thing. Industry seems boring if you're not into sport and into modeling in general not just linear regression.
- j7ake 10y agoRegression beats neural networks when you care about your model parameters.
- digitalzombie 10y ago> For whatever reason, statistics seems to be in the midst of a serious branding problem. Many of them don't know how to code. And the stat program of most of these universities doesn't require any real coding classes. The professor give you code templates and you tweak it to get the result. But the stat classes are really good to handle data. Data science in general like Chapman Hill and UC Irvine seems like a really wide range of stuff from data analyst, a lil programming, data visualization, etc... I don't feel like it goes deep enough in what Statistic does imo. Most are stuck with Tableau and SAS. Pharmacy companies uses SAS. Startup companies and tech companies uses Python and R. I'm speaking from one data point sample, my current stat grad program. My undergrad is comp sci and I did a few years in the tech industry before coming back to grad school.
- et-al 10y ago> I'm speaking from one data point sample, my current stat grad program. My undergrad is comp sci and I did a few years in the tech industry before coming back to grad school. If you don't mind sharing details; why'd you choose your current grad program?