However, the traditional logistic regression model has two obvious shortcomings, mainly in the following two aspects: 1. Feature selection problem. All or most of the feature coefficients obtained by fitting the logistic regression model are not zero, i.e. all most of the features are related ...
Estimate the group l1 penalized LN/LNM model. This research, is entittled "Revitalization Effort of the Role of Subak in the Preservation of Environmental". The degradation of the environmental and the weaknesses of Subak's values which actually support environmental preservation be... FAN XIA ...
In order to discover structure information among data and improve learning performance, we propose a structured penalized logistic regression model which simultaneously performs feature selection and model learning for gene expression data analysis. An efficient coordinate descent algorithm has been developed ...
In this paper wedevelop a new penalized regression model for the NBA, use cross-validation toselect its tuning parameters, and then use it to produce ratings of playerability. We then apply the model to the 2010-2011 NBA season to predict theoutcome of games. We compare the performance of...
When the response variable yi is binary (1 or 0 corresponding to OSCC and normal, in our setting), there is a version of Elastic-net that replaces the squared error loss in the above equation with the negative log-likelihood loss under a logistic regression model, assuming that the logit ...
Select variables and fit the model simultaneously – two birds with one stone! Provide stable results and high prediction accuracy Are computationally very efficient! Do not require pre-training reduction! Interpretability Penalized regression methods fit linear or logistic regression ...
We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each ...
Any estimate of cardiovascular risk is currently based on the use of statistical models inferred from cohort data with methods such as logistic regression. Although attractively simple, the logistic model fails in some situations: 1) If the number of prognostic factors is large (with respect to ...
In recent years, several biomedical studies have used an approach known as multi-view stacking (MVS), where a model is trained on each view separately and the resulting predictions are combined through stacking. In these studies, MVS has been shown to increase classification accuracy. However, ...
This highlights the need to develop and implement statistical methods that can take these tendencies into account.#We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case–control studies. It allows for ...