3
votes
0answers
80 views

Variable reduction by means of ANOVA?

I have a typical problem with several variables and a large amount of data which are not important right now. The goal of the study is to relate variable $Y$ with variables $X_1,X_2,...,X_n$. I have ...
0
votes
1answer
118 views

How to model a multi-dimensional feature set for classification

I am new to statistical modelling and so please pardon if the question appears trivial. I have a set of multi-dimensional data ($T$) where each dimension represents features ($f_i$) obtained from a ...
0
votes
1answer
71 views

Regression - Dealing with Correlated, Zero-Sum Predictors

I'm currently working on a regression problem where a subset of the predictor variables are zero-sum. By zero-sum I don't mean they all sum to zero, I simply mean that increasing one implies a ...
1
vote
1answer
285 views

Not all Features Selected by GLMNET Considered Signficant by GLM (Logistic Regression)

I wanted to create a predictive model of mortality after patients had undergone a surgical procedure. But I also wanted to avoid doing what most researchers do by first performing univariate analysis ...
1
vote
2answers
106 views

BMI at baseline & followup with exposure at baseline; model change or BMI at FUP? Control for BMI baseline?

For a prospective occupational cohort where everyone is exposed to one or more chemical agents, examining BMI at follow-up compared to a specific chemical exposure at baseline, is it necessary to ...
0
votes
3answers
288 views

Classifier feature importance

If I train a GNB/LDA/kNN/other classifier I would like to know, in the model built, how important are features to classify or which feature(s) drives the classifier. For example in SVM models the ...
1
vote
0answers
55 views

What are some common tools, initial approaches to data in a prediction problem when facing too many predictors?

If one is given several hundred features (of both categorical and continuous type) what are some approaches to determining which features to keep or even drop? Data as such is difficult to visualize ...
19
votes
3answers
3k views

Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?

In what circumstances would you want to, or not want to scale or standardize a variable prior to model fitting? And what are the advantages / disadvantages of scaling a variable?
17
votes
3answers
1k views

Why is variable selection necessary?

Common data-based variable selection procedures (for example, forward, backward, stepwise, all subsets) tend to yield models with undesirable properties, including: Coefficients biased away from ...
2
votes
2answers
427 views

Variables importance: who can do the most pushups?

I don't know enough math to formulate an intelligent question on this so I'll give an example. I'd like an answer to my example but also I'd like to know the jargon I need to be able to research it ...
7
votes
2answers
872 views

Computing best subset of predictors for linear regression

For the selection of predictors in multivariate linear regression with $p$ suitable predictors, what methods are available to find an 'optimal' subset of the predictors without explicitly testing all ...