Journal
JOURNAL OF NONPARAMETRIC STATISTICS
Volume 25, Issue 1, Pages 109-128Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10485252.2012.735233
Keywords
generalised additive model; leveraged buyout; local polynomial; mergers and acquisitions; parametric guide
Categories
Funding
- NSF [DMS-0704337, DMS-1206464, DMS-0905561, DMS-1055210]
- NIH/NIEHS [R00ES017744]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0905561] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1055210, 1206464] Funding Source: National Science Foundation
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Generalised nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modelling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalised additive model and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions.
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