Non linear parametric mode regression

Salah Khardani; Anne Françoise Yao

doi:10.1080/03610926.2014.1002940

Non linear parametric mode regression

Salah Khardani
, Anne Françoise Yao

Laboratoire de Physique-Mathématique
Clermont-Auvergne University

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

16 Citations (Scopus)

Résumé

In this article, we propose a semi-parametric mode regression for a non linear model. We use an expectation-maximization algorithm in order to estimate the regression coefficients of modal non linear regression. We also establish asymptotic properties for the proposed estimator under assumptions of the error density. We investigate the performance through a simulation study.

langue originale	Anglais
Pages (de - à)	3006-3024
Nombre de pages	19
journal	Communications in Statistics - Theory and Methods
Volume	46
Numéro de publication	6
Les DOIs	https://doi.org/10.1080/03610926.2014.1002940
état	Publié - 19 mars 2017
Modification externe	Oui

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10.1080/03610926.2014.1002940

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title = "Non linear parametric mode regression",

abstract = "In this article, we propose a semi-parametric mode regression for a non linear model. We use an expectation-maximization algorithm in order to estimate the regression coefficients of modal non linear regression. We also establish asymptotic properties for the proposed estimator under assumptions of the error density. We investigate the performance through a simulation study.",

keywords = "Asymptotic normality, Kernel smoothing, Mode estimation, Non linear model, Parametric regression",

author = "Salah Khardani and Yao, \{Anne Fran{\c c}oise\}",

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year = "2017",

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T1 - Non linear parametric mode regression

AU - Khardani, Salah

AU - Yao, Anne Françoise

PY - 2017/3/19

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AB - In this article, we propose a semi-parametric mode regression for a non linear model. We use an expectation-maximization algorithm in order to estimate the regression coefficients of modal non linear regression. We also establish asymptotic properties for the proposed estimator under assumptions of the error density. We investigate the performance through a simulation study.

KW - Asymptotic normality

KW - Kernel smoothing

KW - Mode estimation

KW - Non linear model

KW - Parametric regression

U2 - 10.1080/03610926.2014.1002940

DO - 10.1080/03610926.2014.1002940

M3 - Article

AN - SCOPUS:85002194601

SN - 0361-0926

VL - 46

SP - 3006

EP - 3024

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 6

ER -

Non linear parametric mode regression

Résumé

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