Additive Models

Mammen, Enno; Park, Byeong U.; Schienle, Melanie

doi:10.1093/oxfordhb/9780199857944.013.007

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

Publication Types

Select...

Other3

Article1

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Operational time and in-sample density forecasting

Lee¹,

Mammen²,

Nielsen³

et al. 2017

Ann. Statist.

Self Cite

View full text Add to dashboard Cite

This is the published version of the paper.This version of the publication may differ from the final published version. In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Permanent repository link

show abstract