1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia


Opción 1: Descargar recurso

Detalles del recurso

Descripción

In this paper, we cluster profiles of longitudinal data using a penalized regression method. Specifically, we allow heterogeneous variation of longitudinal patterns for each subject, and utilize a pairwise-grouping penalization on coefficients of the nonparametric B-spline models to form subgroups. Consequently, we identify clusters based on different patterns of the predicted longitudinal curves. One advantage of the proposed method is that there is no need to pre-specify the number of clusters; instead the number of clusters is selected automatically through a model selection criterion. Our method is also applicable for unbalanced data where different subjects could have measurements at different time points. To implement the proposed method, we develop an alternating direction method of multipliers (ADMM) algorithm which has the desirable convergence property. In theory, we establish the consistency properties for approximated nonparametric function estimation and subgrouping memberships. In addition, we show that our method outperforms the existing competitive approaches in our simulation studies and real data example.

Pertenece a

Project Euclid (Hosted at Cornell University Library)  

Autor(es)

Zhu, Xiaolu -  Qu, Annie - 

Id.: 70942082

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveADMM - 

Tipo de recurso: Text  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: Copyright 2018 The Institute of Mathematical Statistics and the Bernoulli Society

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] 1935-7524

Fecha de contribución: 22-may-2018

Contacto:

Localización:
* Electron. J. Statist. 12, no. 1 (2018), 171-193
* doi:10.1214/17-EJS1389

Otros recursos que te pueden interesar

  1. Graphical model selection with latent variables Gaussian graphical models are commonly used to characterize the conditional dependence among variabl...

Otros recursos de la mismacolección

  1. Slice inverse regression with score functions We consider non-linear regression problems where we assume that the response depends non-linearly on...
  2. Dimension reduction-based significance testing in nonparametric regression A dimension reduction-based adaptive-to-model test is proposed for significance of a subset of covar...
  3. High-dimensional robust precision matrix estimation: Cellwise corruption under $\epsilon $-contamination We analyze the statistical consistency of robust estimators for precision matrices in high dimension...
  4. A two stage $k$-monotone B-spline regression estimator: Uniform Lipschitz property and optimal convergence rate This paper considers $k$-monotone estimation and the related asymptotic performance analysis over a ...
  5. Uniformly valid confidence sets based on the Lasso In a linear regression model of fixed dimension $p\leq n$, we construct confidence regions for the u...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.