Variable selection in classification for multivariate functional data
نویسندگان
چکیده
منابع مشابه
Variable selection for multivariate failure time data.
In this paper, we proposed a penalised pseudo-partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients. Under certain regularity conditions, we show the consistency and asymptotic normality of the penalised likelihood estimators. We further demonstrate that, for certain penalty functions with proper choices of regulari...
متن کاملVariable Selection for Multivariate Survival data
It is assumed for the Cox’s proportional hazards model that the survival times of subjects are independent. This assumption might be violated in some situations, in which the collected data are correlated. The well-known Cox model is not valid in this situation because independence assumption among individuals is violated. For this purpose Cox’s proportional hazard model is extent to the analys...
متن کاملVariable selection in functional data classification: a maxima hunting proposal
Variable selection is considered in the setting of supervised binary classification with functional data {X(t), t ∈ [0, 1]}. By “variable selection” we mean any dimensionreduction method which leads to replace the whole trajectory {X(t), t ∈ [0, 1]}, with a low-dimensional vector (X(t1), . . . , X(td)) still keeping a similar classification error. Our proposal for variable selection is based on...
متن کاملVariable Selection for High Dimensional Multivariate Outcomes.
We consider variable selection for high-dimensional multivariate regression using penalized likelihoods when the number of outcomes and the number of covariates might be large. To account for within-subject correlation, we consider variable selection when a working precision matrix is used and when the precision matrix is jointly estimated using a two-stage procedure. We show that under suitabl...
متن کاملVariable 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 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: Information Sciences
سال: 2019
ISSN: 0020-0255
DOI: 10.1016/j.ins.2018.12.060