Variable selection in multivariate multiple regression
نویسندگان
چکیده
منابع مشابه
Variable Selection for Multivariate Logistic Regression Models
In this paper, we use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on th...
متن کاملBayesian variable selection for multivariate spatially varying coefficient regression.
Physical activity has many well-documented health benefits for cardiovascular fitness and weight control. For pregnant women, the American College of Obstetricians and Gynecologists currently recommends 30 minutes of moderate exercise on most, if not all, days; however, very few pregnant women achieve this level of activity. Traditionally, studies have focused on examining individual or interpe...
متن کاملSparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection in Multivariate Regression
The reduced-rank regression is an effective method to predict multiple response variables from the same set of predictor variables, because it can reduce the number of model parameters as well as take advantage of interrelations between the response variables and therefore improve predictive accuracy. We propose to add a new feature to the reduced-rank regression that allows selection of releva...
متن کاملVariable Selection in ROC Regression
Regression models are introduced into the receiver operating characteristic (ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable selections to two models will bring barriers...
متن کاملVariable Selection in Quantile Regression
After its inception in Koenker and Bassett (1978), quantile regression has become an important and widely used technique to study the whole conditional distribution of a response variable and grown into an important tool of applied statistics over the last three decades. In this work, we focus on the variable selection aspect of penalized quantile regression. Under some mild conditions, we demo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2020
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0236067