Encyclopedia of Gerontology and Population Aging

Living Edition
| Editors: Danan Gu, Matthew E. Dupre

Semiparametric Methods

  • Justin T. Max
  • Emma ZangEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-69892-2_561-1

Definition

Semiparametric methods are statistical methods which make use of both parametric and nonparametric components. In particular, they avoid assumptions which fully characterize the distribution of the data while still imposing a minimal structure. Although semiparametric models offer a diverse array of statistical applications, the Cox proportional hazard model and the group-based model for latent heterogeneity in trajectories within populations are commonly used ones and have proven to be particularly valuable in gerontological research.

Overview

The notion of a semiparametric model can be understood by first observing the extreme cases of parametric and nonparametric models. Consider the problem of modeling an outcome y as a function of a set of covariates X. The standard linear regression model where y =  + ϵ and the components of ϵ are i.i.d. N(02) is a simple example of a parametric model. On the other extreme are nonparametric models which assume little more than y = 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Capital One Financial CorporationRichmondUSA
  2. 2.Department of SociologyYale UniversityNew HavenUSA

Section editors and affiliations

  • Kenneth C. Land
    • 1
  • Anthony R. Bardo
    • 2
  1. 1.Department of Sociology and Social Science Research InstituteDuke UniversityDurhamUSA
  2. 2.Department of SociologyUniversity of KentuckyLexingtonUSA