纵向和聚类相关数据中的群体平均推理的边际加性模型

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Glen Mcgee, Alex Stringer
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引用次数: 0

摘要

我们提出了一种新的边际加性模型(MAM),用于建模具有非线性总体平均关联的聚类相关数据。所提出的MAM是边际均值模型的估计和不确定性量化的统一框架,结合了聚类间变异性和聚类特定预测的推断。我们提出了一种拟合算法,该算法能够有效地计算标准误差并校正惩罚项的估计。我们在模拟中展示了所提出的方法,并将其应用于(i)海狸觅食行为的纵向研究,以及(ii)西非泥鳅感染的空间分析。本文受版权保护。保留所有权利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data
We propose a novel marginal additive model (MAM) for modelling cluster‐correlated data with non‐linear population‐averaged associations. The proposed MAM is a unified framework for estimation and uncertainty quantification of a marginal mean model, combined with inference for between‐cluster variability and cluster‐specific prediction. We propose a fitting algorithm that enables efficient computation of standard errors and corrects for estimation of penalty terms. We demonstrate the proposed methods in simulations and in application to (i) a longitudinal study of beaver foraging behaviour, and (ii) a spatial analysis of Loaloa infection in West Africa.This article is protected by copyright. All rights reserved.
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来源期刊
Scandinavian Journal of Statistics
Scandinavian Journal of Statistics 数学-统计学与概率论
CiteScore
1.80
自引率
0.00%
发文量
61
审稿时长
6-12 weeks
期刊介绍: The Scandinavian Journal of Statistics is internationally recognised as one of the leading statistical journals in the world. It was founded in 1974 by four Scandinavian statistical societies. Today more than eighty per cent of the manuscripts are submitted from outside Scandinavia. It is an international journal devoted to reporting significant and innovative original contributions to statistical methodology, both theory and applications. The journal specializes in statistical modelling showing particular appreciation of the underlying substantive research problems. The emergence of specialized methods for analysing longitudinal and spatial data is just one example of an area of important methodological development in which the Scandinavian Journal of Statistics has a particular niche.
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