Influence diagnostics in semiparametric regression models

Choongrak Kim, Byeong U. Park and Woochul Kim

Statistics & Probability Letters, 2002, vol. 60, issue 1, 49-58

Abstract: In this paper we consider the semiparametric regression model, Y=x'[beta]+m(t)+[var epsilon], and provide some influence diagnostics for estimators of [beta], m and the mean response x'[beta]+m(t). We express these influence diagnostics as functions of the residuals and leverages. We find that an influential observation on the estimator of the coefficient vector [beta] may not be influential on that of the nonparametric component m, and vice versa. Also, an observation which is not influential on each of them may be influential on the estimator of the mean response. Therefore, influence of an observation should be evaluated on each estimator separately. An illustrative example based on a real data set is also given.

Keywords: Bandwidth; Cross-validation; Hat; matrix; Influential; observations; Local; polynomial; Smoothing; spline (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(02)00268-7
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:60:y:2002:i:1:p:49-58

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().