Question
Robert Tibshirani coined the name for this statistical method in a 1996 JRSS-B article, where he noted the favorable properties of this method’s use of an L1 penalty. For 10 points each:
[10h] Name this technique, which in the Elastic Net is functionally combined with a similar method that uses an L2 penalty. This technique’s L1 penalty shrinks some coefficients to true zero and performs subset selection.
ANSWER: LASSO [or Least Absolute Shrinkage and Selection Operator; prompt on linear regression; prompt on penalized regression]
[10m] This data resampling technique is often used to find a penalty parameter for LASSO. The statistics section of Stack Exchange is named for this technique, which comes in K-fold and Leave-one-out varieties.
ANSWER: cross-validation [or CV; accept K-fold cross-validation; accept Leave-one-out cross-validation or LOOCV; accept Cross Validated; prompt on validate or validation or validating a model]
[10e] LASSO is an alternative to stepwise selection, which often selects variables with this property of a p-value being below a chosen cutoff alpha like .05 (“point zero five”) and hence rejecting a null hypothesis.
ANSWER: statistical significance [or statistically significant; accept Null Hypothesis Significance Testing]
<JF, Other Science: Math>
Summary
| 2023 ARCADIA at Imperial | Imperial | Y | 5 | 8.00 | 60% | 20% | 0% |
Data
| Birmingham | Bristol | 0 | 0 | 10 | 10 |
| Durham | Cambridge A | 0 | 0 | 0 | 0 |
| Warwick | Cambridge B | 0 | 0 | 10 | 10 |
| Oxford | Imperial A | 0 | 10 | 0 | 10 |
| Edinburgh | Imperial B | 0 | 0 | 10 | 10 |