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Lee, Young K.; Mammen, Enno; Nielsen, Jens P.; Park, Byeong U.

Generalised additive dependency inflated models including aggregated covariates. (English) Zbl 1416.62218

Electron. J. Stat. 13, No. 1, 67-93 (2019).
The “Generalised Additive Dependency Inflated Model including Aggregated Covariance” (GADIMAC) model has the form \[ U = G(m_0 + m_1(X) + m_2(\lambda(X)Y) + m_3(X+Y)) + \epsilon, \] where \(X, Y, U\) are observable, \(G\) is a known invertible link function, \(m_0\) is an unknown constant, and \(m_1, m_2, m_3, \lambda\) are unknown functions. The paper under review presents results on the rate of a penalized least squares estimator of \((m_0, m_1, m_2, m_3, \lambda)\) and on the identification of those functions. Furthermore, an age-period-cohort density version of GADIMAC is applied to forecast future asbestos-related deaths in the UK.
Reviewer: Michael Stolz (Münster)

MSC:

62G08 Nonparametric regression and quantile regression
62G20 Asymptotic properties of nonparametric inference
62P10 Applications of statistics to biology and medical sciences; meta analysis
62M20 Inference from stochastic processes and prediction

Keywords:

structured nonparametric models; age-period-cohort model; identifiability; B-splines; UK mesothelioma mortality
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Full Text: DOI Euclid

References:

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