Penalized regression models for patent keyword analysis

2017 ◽  
Vol 12 (3) ◽  
pp. 239-244 ◽  
Author(s):  
Jong-Min Kim ◽  
Jea-Bok Ryu ◽  
Seung-Joo Lee ◽  
Sunghae Jun
Keyword(s):  
Regression Models ◽  
Penalized Regression ◽  
Keyword Analysis

Penalized Regression Models to Select Biomarkers of Environmental Enteropathy Associated with Linear Growth Acquisition in a Peruvian Birth Cohort

2019 ◽  
Author(s):  
Josh Colston ◽  
Pablo Peñataro Yori ◽  
Lawrence H. Moulton ◽  
Maribel Paredes Olortegui ◽  
Peter S. Kosek ◽  
...  
Keyword(s):  
Birth Cohort ◽  
Regression Models ◽  
Linear Growth ◽  
Penalized Regression ◽  
Environmental Enteropathy

scholarly journals Survival Analysis by Penalized Regression and Matrix Factorization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yeuntyng Lai ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Matrix Factorization ◽  
Regression Models ◽  
Nonnegative Matrix ◽  
B Cell Lymphoma ◽  
Penalized Regression ◽  
Survival Prediction ◽  
Suitable Model ◽  
Gene Expressions ◽  
Combination Methods ◽  
Survival Patterns

Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We triedL1- (lasso),L2- (ridge), andL1-L2combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found thatL1-L2combined method predicts survival best with the smallest logrankPvalue. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrankPvalues. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.

Penalized Regression Models

2019 ◽  
pp. 179-206
Author(s):  
Taylor Arnold ◽  
Michael Kane ◽  
Bryan W. Lewis
Keyword(s):  
Regression Models ◽  
Penalized Regression

Penalized regression models with autoregressive error terms

2013 ◽  
Vol 83 (9) ◽  
pp. 1756-1772 ◽  
Author(s):  
Young Joo Yoon ◽  
Cheolwoo Park ◽  
Taewook Lee
Keyword(s):  
Regression Models ◽  
Penalized Regression ◽  
Error Terms

scholarly journals A Bayesian mixture model for changepoint estimation using ordinal predictors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Emily Roberts ◽  
Lili Zhao
Keyword(s):  
Mixture Model ◽  
Regression Models ◽  
Performance Status ◽  
Predictor Variable ◽  
Penalized Regression ◽  
Continuous Variable ◽  
Ecog Performance Status ◽  
Threshold Detection ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture

Abstract In regression models, predictor variables with inherent ordering, such ECOG performance status or novel biomarker expression levels, are commonly seen in medical settings. Statistically, it may be difficult to determine the functional form of an ordinal predictor variable. Often, such a variable is dichotomized based on whether it is above or below a certain cutoff. Other methods conveniently treat the ordinal predictor as a continuous variable and assume a linear relationship with the outcome. However, arbitrarily choosing a method may lead to inaccurate inference and treatment. In this paper, we propose a Bayesian mixture model to consider both dichotomous and linear forms for the variable. This allows for simultaneous assessment of the appropriate form of the predictor in regression models by considering the presence of a changepoint through the lens of a threshold detection problem. This method is applicable to continuous, binary, and survival outcomes, and it is easily amenable to penalized regression. We evaluated the proposed method using simulation studies and apply it to two real datasets. We provide JAGS code for easy implementation.

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