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: 17-feb-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. Large and moderate deviations for kernel–type estimators of the mean density of Boolean models The mean density of a random closed set with integer Hausdorff dimension is a crucial notion in stoc...
  2. Regularity properties and simulations of Gaussian random fields on the sphere cross time We study the regularity properties of Gaussian fields defined over spheres cross time. In particular...
  3. Kernel estimation of extreme regression risk measures The Regression Conditional Tail Moment (RCTM) is the risk measure defined as the moment of order $b\...
  4. Statistical properties of simple random-effects models for genetic heritability Random-effects models are a popular tool for analysing total narrow-sense heritability for quantitat...
  5. Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding In the random coefficients binary choice model, a binary variable equals 1 iff an index $X^{\top}\be...

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.