Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Limit Imbalanced Logistic Regression by Binary Predictors   In this work, we introduce a modified (rescaled) likelihood for imbalanced
logistic regression. This new approach makes easier the use of exponential
priors and the computation of lasso regularization path. Precisely, we study a
limiting behavior for which class imbalance is artificially increased by
replication of the majority class observations. If some strong overlap
conditions are satisfied, the maximum likelihood estimate converges towards a
finite value close to the initial one (intercept excluded) as shown by
simulations with binary predictors. This solution corresponds to the extremum
of a concave function that we refer to as "rescaled" likelihood. In this
context, the use of exponential priors has a clear interpretation as a shift on
the predictor means for the minority class. Thanks to the simple binary
structure, some random designs give analytic path estimators for the lasso
regularization problem. An effective approximate path algorithm by piecewise
logarithmic functions based on matrix inversions is also presented. This work
was motivated by its potential application to spontaneous reports databases in
a pharmacovigilance context.

28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org