Generalized Single Index Models and Jensen Effects on Reproduction and Survival

Zi Ye, Giles Hooker () and Stephen P. Ellner
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Zi Ye: Cornell University
Giles Hooker: Cornell University
Stephen P. Ellner: Cornell University

Journal of Agricultural, Biological and Environmental Statistics, 2021, vol. 26, issue 3, No 9, 492-512

Abstract: Abstract Environmental variability often has substantial impacts on natural populations and communities through its effects on the performance of individuals. Because organisms’ responses to environmental conditions are often nonlinear (e.g., decreasing performance on both sides of an optimal temperature), the mean response is often different from the response in the mean environment. Ye et al. (Ann Appl Stat 14(3):1326–12341, 2020) proposed testing for the presence of such variance effects on individual or population growth rates by estimating the “Jensen Effect”, the difference in average growth rates under varying versus fixed environments, in functional single index models for environmental effects on growth. In this paper, we extend this analysis to effects of environmental variance on reproduction and survival, which have count and binary outcomes. In the standard generalized linear models used to analyze such data the direction of the Jensen Effect is tacitly assumed a priori by the model’s link function. Here we extend the methods of Ye et al. (2020) using a generalized single index model to test whether this assumed direction is contradicted by the data. We show that our test has reasonable power under mild alternatives, but requires sample sizes that are larger than are often available. We demonstrate our methods on a long-term time series of plant ground cover in Idaho sagebrush steppe.

Keywords: Spline smoothing; Nonparametric inference; SiZer; Relative nonlinearity; Delta method (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13253-021-00452-4

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