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Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Constructing Graphical Models via the Focused Information Criterion

Authors : Gerda Claeskens, Eugen Pircalabelu, Lourens Waldorp

Published in: Modeling and Stochastic Learning for Forecasting in High Dimensions

Publisher: Springer International Publishing

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Abstract

A focused information criterion is developed to estimate undirected graphical models where for each node in the graph a generalized linear model is put forward conditioned upon the other nodes in the graph. The proposed method selects a graph with a small estimated mean squared error for a user-specified focus, which is a function of the parameters in the generalized linear models, by selecting an appropriate model at each node. For situations where the number of nodes is large in comparison with the number of cases, the procedure performs penalized estimation with quadratic approximations to several popular penalties. To show the procedure’s applicability and usefulness we have applied it to two datasets involving voting behavior of U.S. senators and to a clinical dataset on psychopathology.

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previous chapter Modelling and Forecasting Daily Electricity Load via Curve Linear Regression
next chapter Fully Nonparametric Short Term Forecasting Electricity Consumption
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Metadata
Title
Constructing Graphical Models via the Focused Information Criterion
Authors
Gerda Claeskens
Eugen Pircalabelu
Lourens Waldorp
Copyright Year
2015
Book
Modeling and Stochastic Learning for Forecasting in High Dimensions

Print ISBN: 978-3-319-18731-0

Electronic ISBN: 978-3-319-18732-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-18732-7_4

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