Add to ORKGThe impact of sparse data conditions was examined among one or more predictor variables in logistic regression and assessed the effectiveness of the Firth (1993) procedure in reducing potential parameter estimation bias. Results indicated sparseness in binary predictors introduces bias that is substantial with small sample sizes, and the Firth procedure can effectively correct this bias
Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcom...
Poisson regression can be challenging with sparse data, in particular with certain data constellatio...
There is no phenomenal method practitioners can use as a appropriate tool for model validation when ...
Conditional logistic regression was developed to avoid "sparse-data " biases that can aris...
Abstract Background When developing risk models for binary data with small or sparse data sets, the ...
Every day, traditional statistical methodology are used world wide to study a variety of topics and ...
In knowledge-based systems, besides obtaining good output prediction accuracy, it is crucial to unde...
Logistic regression is one of the most popular techniques used to describe the relationship between ...
Correlation between a categorical response variable and one or several predictor variables involving...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
Abstract Background For finite samples with binary outcomes penalized logistic regression such as ri...
Although popular and extremely well established in mainstream statistical data analysis, logistic re...
We demonstrate and analyze an aggregation method for sparse logistic regression in high-dimensional ...
This study aims to illustrate the problem of (Quasi) Complete Separation in the sparse data pattern ...
In this thesis, sparse logistic regression models are applied in a set of real world machine learnin...
Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcom...
Poisson regression can be challenging with sparse data, in particular with certain data constellatio...
There is no phenomenal method practitioners can use as a appropriate tool for model validation when ...
Conditional logistic regression was developed to avoid "sparse-data " biases that can aris...
Abstract Background When developing risk models for binary data with small or sparse data sets, the ...
Every day, traditional statistical methodology are used world wide to study a variety of topics and ...
In knowledge-based systems, besides obtaining good output prediction accuracy, it is crucial to unde...
Logistic regression is one of the most popular techniques used to describe the relationship between ...
Correlation between a categorical response variable and one or several predictor variables involving...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
Abstract Background For finite samples with binary outcomes penalized logistic regression such as ri...
Although popular and extremely well established in mainstream statistical data analysis, logistic re...
We demonstrate and analyze an aggregation method for sparse logistic regression in high-dimensional ...
This study aims to illustrate the problem of (Quasi) Complete Separation in the sparse data pattern ...
In this thesis, sparse logistic regression models are applied in a set of real world machine learnin...
Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcom...
Poisson regression can be challenging with sparse data, in particular with certain data constellatio...
There is no phenomenal method practitioners can use as a appropriate tool for model validation when ...