A semiparametric mixture regression model for longitudinal dataCitation formats

  • Authors:
  • Tapio Nummi
  • Janne Salonen
  • Lasse Koskinen
  • Jianxin Pan

Standard

A semiparametric mixture regression model for longitudinal data. / Nummi, Tapio; Salonen, Janne; Koskinen, Lasse; Pan, Jianxin.

In: Journal of Statistical Theory and Practice, Vol. 12, No. 1, 2018, p. 12-22.

Research output: Contribution to journalArticle

Harvard

Nummi, T, Salonen, J, Koskinen, L & Pan, J 2018, 'A semiparametric mixture regression model for longitudinal data' Journal of Statistical Theory and Practice, vol. 12, no. 1, pp. 12-22. https://doi.org/10.1080/15598608.2017.1298062

APA

Nummi, T., Salonen, J., Koskinen, L., & Pan, J. (2018). A semiparametric mixture regression model for longitudinal data. Journal of Statistical Theory and Practice, 12(1), 12-22. https://doi.org/10.1080/15598608.2017.1298062

Vancouver

Nummi T, Salonen J, Koskinen L, Pan J. A semiparametric mixture regression model for longitudinal data. Journal of Statistical Theory and Practice. 2018;12(1):12-22. https://doi.org/10.1080/15598608.2017.1298062

Author

Nummi, Tapio ; Salonen, Janne ; Koskinen, Lasse ; Pan, Jianxin. / A semiparametric mixture regression model for longitudinal data. In: Journal of Statistical Theory and Practice. 2018 ; Vol. 12, No. 1. pp. 12-22.

Bibtex

@article{59f60fd28cb24b0db343f774f125dd5f,
title = "A semiparametric mixture regression model for longitudinal data",
abstract = "A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized likelihood method with the EM-algorithm. A computationally feasible alternative method that provides an approximate solution is also introduced. Simulation experiments and real data example are used to illustrate the methods.",
author = "Tapio Nummi and Janne Salonen and Lasse Koskinen and Jianxin Pan",
year = "2018",
doi = "10.1080/15598608.2017.1298062",
language = "English",
volume = "12",
pages = "12--22",
journal = "Journal of Statistical Theory and Practice",
issn = "1559-8608",
publisher = "Taylor & Francis",
number = "1",

}

RIS

TY - JOUR

T1 - A semiparametric mixture regression model for longitudinal data

AU - Nummi, Tapio

AU - Salonen, Janne

AU - Koskinen, Lasse

AU - Pan, Jianxin

PY - 2018

Y1 - 2018

N2 - A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized likelihood method with the EM-algorithm. A computationally feasible alternative method that provides an approximate solution is also introduced. Simulation experiments and real data example are used to illustrate the methods.

AB - A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized likelihood method with the EM-algorithm. A computationally feasible alternative method that provides an approximate solution is also introduced. Simulation experiments and real data example are used to illustrate the methods.

U2 - 10.1080/15598608.2017.1298062

DO - 10.1080/15598608.2017.1298062

M3 - Article

VL - 12

SP - 12

EP - 22

JO - Journal of Statistical Theory and Practice

JF - Journal of Statistical Theory and Practice

SN - 1559-8608

IS - 1

ER -