Implementation of higher-order asymptotics to S-plus

Grace Y. Yi; Jianrong Wu; Ying Liu

doi:10.1016/S0167-9473(02)00096-8

Implementation of higher-order asymptotics to S-plus

Grace Y. Yi, Jianrong Wu, Ying Liu

Internal Medicine

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Higher-order asymptotics is an active area of development in theoretical statistics. However, most existing work in higher-order asymptotics is directed to the theoretical aspects. This paper attempts to incorporate higher-order inference procedures to S-plus, a widely used software in statistics. Algorithm is developed in the settings of generalized linear models and nonlinear regression models. The proposed algorithm generalizes the standard S-plus functions _glimand "nls" in the sense that both the first-order and higher-order p-values are provided, and its manipulation is straightforward.

Original language	English
Pages (from-to)	775-800
Number of pages	26
Journal	Computational Statistics and Data Analysis
Volume	40
Issue number	4
DOIs	https://doi.org/10.1016/S0167-9473(02)00096-8
State	Published - Oct 28 2002

Keywords

Generalized linear model
Higher-order asymptotics
Interest parameter
Link function
Nonlinear regression model
p-value

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/S0167-9473(02)00096-8

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AU - Wu, Jianrong

AU - Liu, Ying

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N2 - Higher-order asymptotics is an active area of development in theoretical statistics. However, most existing work in higher-order asymptotics is directed to the theoretical aspects. This paper attempts to incorporate higher-order inference procedures to S-plus, a widely used software in statistics. Algorithm is developed in the settings of generalized linear models and nonlinear regression models. The proposed algorithm generalizes the standard S-plus functions glim and "nls" in the sense that both the first-order and higher-order p-values are provided, and its manipulation is straightforward.

AB - Higher-order asymptotics is an active area of development in theoretical statistics. However, most existing work in higher-order asymptotics is directed to the theoretical aspects. This paper attempts to incorporate higher-order inference procedures to S-plus, a widely used software in statistics. Algorithm is developed in the settings of generalized linear models and nonlinear regression models. The proposed algorithm generalizes the standard S-plus functions glim and "nls" in the sense that both the first-order and higher-order p-values are provided, and its manipulation is straightforward.

KW - Generalized linear model

KW - Higher-order asymptotics

KW - Interest parameter

KW - Link function

KW - Nonlinear regression model

KW - p-value

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JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

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ER -

Implementation of higher-order asymptotics to S-plus

Abstract

Keywords

ASJC Scopus subject areas

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