Moment-based Estimation of Mixtures of Regression Models

Research output: Contribution to journal › Journal article › Research

Standard

Moment-based Estimation of Mixtures of Regression Models. / Ekstrøm, Claus Thorn; Pipper, Christian Bressen.

In: arXiv, 2019.

Research output: Contribution to journal › Journal article › Research

Harvard

Ekstrøm, CT & Pipper, CB 2019, 'Moment-based Estimation of Mixtures of Regression Models', arXiv.

APA

Ekstrøm, C. T., & Pipper, C. B. (2019). Moment-based Estimation of Mixtures of Regression Models. arXiv.

Vancouver

Ekstrøm CT, Pipper CB. Moment-based Estimation of Mixtures of Regression Models. arXiv. 2019.

Author

Ekstrøm, Claus Thorn ; Pipper, Christian Bressen. / Moment-based Estimation of Mixtures of Regression Models. In: arXiv. 2019.

Bibtex

@article{d01439ba4a60425582176d85b41caddd,
title = "Moment-based Estimation of Mixtures of Regression Models",
abstract = " Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, only the average regression model for one of the components in the mixture model is needed and no requirements on the distributions. The consistency and asymptotic distribution of the estimators is derived and the proposed method is validated through a series of simulation studies and is shown to be highly accurate. We illustrate the use of the moment-based mixture of regression models with an application to wine quality data. ",
keywords = "math.ST, stat.AP, stat.ME, stat.TH",
author = "Ekstr{\o}m, {Claus Thorn} and Pipper, {Christian Bressen}",
note = "17 pages, 3 figures",
year = "2019",
language = "English",
journal = "arXiv",

}

RIS

TY - JOUR

T1 - Moment-based Estimation of Mixtures of Regression Models

AU - Ekstrøm, Claus Thorn

AU - Pipper, Christian Bressen

N1 - 17 pages, 3 figures

PY - 2019

Y1 - 2019

N2 - Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, only the average regression model for one of the components in the mixture model is needed and no requirements on the distributions. The consistency and asymptotic distribution of the estimators is derived and the proposed method is validated through a series of simulation studies and is shown to be highly accurate. We illustrate the use of the moment-based mixture of regression models with an application to wine quality data.

AB - Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, only the average regression model for one of the components in the mixture model is needed and no requirements on the distributions. The consistency and asymptotic distribution of the estimators is derived and the proposed method is validated through a series of simulation studies and is shown to be highly accurate. We illustrate the use of the moment-based mixture of regression models with an application to wine quality data.

KW - math.ST

KW - stat.AP

KW - stat.ME

KW - stat.TH

M3 - Journal article

JO - arXiv

JF - arXiv

ER -

ID: 225383377