Regression imputation in the functional linear model with missing values in the response

By Christophe Crambes, Yousri Henchiri. Journal of Statistical Planning and Inference, 2019

47 citations recorded by the source corpus.

Abstract We are interested in functional linear regression when some observations of the real response are missing, while the functional covariate is completely observed. A complete case regression imputation method of missing data is presented, using functional principal component regression to estimate the functional coefficient of the model. We study the asymptotic behavior of the error when the missing data are replaced by the regression imputed value, in a ’missing at random’ framework. The completed database can be used to estimate the functional coefficient of the model and to predict new values of the response. The practical behavior of the method is also studied on simulated datasets. A real dataset illustration is performed in the environmental context of air quality.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f31853ea4b3795a8cf1d26f81a83d2a25649989b

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In Statistics and Probability

1. Mphago Banyatsang
2. Tlamelo Emmanuel
3. Dimane Mpoeleng
4. Thabo Semong
5. Oteng Tabona
6. Thabiso M. Maupong

Citations in the corpus

• A survey on missing data in machine learning (2021, methodology): “In another study, the authors proposed a complete case regression missing values handling method using functional principal component [45].”