Literature DB >> 18852827

A tree-based method for modeling a multivariate ordinal response.

Heping Zhang1, Yuanqing Ye.   

Abstract

Motivated by a real example of understanding the so-called "building related occupant complaint syndromes" (BROCS), we propose a tree-based method for analyzing a multivariate ordinal response. Our method is semiparametric by assuming a within-node parametric distribution on the adaptive nonparametric tree framework. We use simulation experiments to demonstrate the ability of our method to identify underlying structures in the data and the fact that analyzing ordinal response data with proper methods that take ordinality into account is considerably more powerful than dichotomization. The reanalysis of the BROCS data also suggests new insights that go beyond a previous analysis based on the dichotomization.

Year:  2008        PMID: 18852827      PMCID: PMC2566541          DOI: 10.4310/sii.2008.v1.n1.a14

Source DB:  PubMed          Journal:  Stat Interface        ISSN: 1938-7989            Impact factor:   0.582


  5 in total

Review 1.  Modelling ordered categorical data: recent advances and future challenges.

Authors:  A Agresti
Journal:  Stat Med       Date:  1999 Sep 15-30       Impact factor: 2.373

2.  Modeling longitudinal data with ordinal response by varying coefficients.

Authors:  G Kauermann
Journal:  Biometrics       Date:  2000-09       Impact factor: 2.571

3.  Detection of genes for ordinal traits in nuclear families and a unified approach for association studies.

Authors:  Heping Zhang; Xueqin Wang; Yuanqing Ye
Journal:  Genetics       Date:  2005-10-11       Impact factor: 4.562

4.  A mixed effects model for multivariate ordinal response data including correlated discrete failure times with ordinal responses.

Authors:  T R Ten Have
Journal:  Biometrics       Date:  1996-06       Impact factor: 2.571

5.  A random-effects ordinal regression model for multilevel analysis.

Authors:  D Hedeker; R D Gibbons
Journal:  Biometrics       Date:  1994-12       Impact factor: 2.571

  5 in total
  5 in total

1.  Maximal conditional chi-square importance in random forests.

Authors:  Minghui Wang; Xiang Chen; Heping Zhang
Journal:  Bioinformatics       Date:  2010-02-03       Impact factor: 6.937

2.  The use of classification trees for bioinformatics.

Authors:  Xiang Chen; Minghui Wang; Heping Zhang
Journal:  Wiley Interdiscip Rev Data Min Knowl Discov       Date:  2011-01-06

3.  Energy bagging tree.

Authors:  Taoyun Cao; Xueqin Wang; Heping Zhang
Journal:  Stat Interface       Date:  2016       Impact factor: 0.582

4.  A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.

Authors:  Jaime Lynn Speiser
Journal:  J Biomed Inform       Date:  2021-03-26       Impact factor: 6.317

5.  Willows: a memory efficient tree and forest construction package.

Authors:  Heping Zhang; Minghui Wang; Xiang Chen
Journal:  BMC Bioinformatics       Date:  2009-05-05       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.