Tagged Questions

2
votes
1answer
83 views

The dangers of stepwise variable selection in regression

This paper discusses some of the dangers of using stepwise variable selection procedures: http://www.auburn.edu/~tds0009/Articles/Whittingham%20et%20al.%202006.pdf I'm struggling to understand ...
1
vote
1answer
51 views

Estimation Technique

My panel regression model is as follows: $$Y_{it}= PS_{it}+PF_{it}+EF_{it}+ e_{it}$$ where $i$ : country $t$ : year $Y_{it}$ : GDP per capita $PS_{it}$ : Political stability $PF_{it}$ : ...
8
votes
1answer
362 views

What is the difference between AIC() and extractAIC() in R?

The R documentation for either does not shed much light. All that I can get from this link is that using either one should be fine. What I do not get is why they are not equal. Fact: The stepwise ...
2
votes
1answer
183 views

Specifying add and drop thresholds for stepwise regression in R

I am running a stepwise regression using the F test as the criterion. Is there a way to explicitly set the add and drop thresholds (alpha levels) in R? The documentation does not make it clear.
2
votes
2answers
246 views

R-code question: model selection based on individual significance in regression?

I'm trying to generate an R function that keeps relevant variables based on their absolute t-value (or p, whichever is easier in code). Basically what I want is to run one regression (1), retain all ...
1
vote
0answers
157 views

Stepwise Regression Models in JMP

In JMP, I am building a regression model by using "Analyze"->"Fit Model" and choosing "Stepwise" for the personality. Once I click "Run" in the "Model Specifications" window, I get the "Fit Stepwise" ...
15
votes
5answers
980 views

Detecting significant predictors out of 300 independent variables

In a dataset of two non-overlapping populations (patients & healthy, total $n=60$) I would like to find (out of $300$ independent variables) significant predictors for a continuous dependent ...
11
votes
1answer
2k views

Estimating R-squared and statistical significance from penalized regression model

I am using the R package penalized to obtain shrunken estimates of coefficients for a dataset where I have lots of predictors and little knowledge of which ones are important. After I've picked tuning ...
4
votes
2answers
2k views

Regression selection using all possible subsets selection and automatic selection techniques

Given the dataset cars.txt, we want to formulate a good regression model for the Midrange Price using the variables Horsepower, Length, Luggage, Uturn, Wheelbase, and Width. Both: using all possible ...
8
votes
1answer
477 views

Sane stepwise regression?

Suppose I want to build a binary classifier. I have several thousand features and only a few 10s of samples. From domain knowledge, I have a good reason to believe that the class label can be ...
4
votes
3answers
739 views

Can you use heteroskedastic time series variables within a regression model?

We are working on a multivariate linear regression model. Our objective is to forecast the quarterly % growth in mortgage loans outstanding. The independent variables are: 1) Dow Jones level. 2) % ...
3
votes
2answers
3k views

Interpreting the drop1 output in R

In R, the drop1command outputs something neat. These two commands should get you some output: example(step)#-> swiss ...