TY - CHAP
T1 - Variable Selection and Feature Screening
AU - Liu, Wanjun
AU - Li, Runze
N1 - Funding Information:
Acknowledgements This work was supported by a NSF grant DMS 1820702 and NIDA, NIH grant P50 DA039838. The content is solely the responsibility of the authors and does not necessarily represent the official views of NSF, NIH, or NIDA.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - This chapter provides a selective review on feature screening methods for ultra-high dimensional data. The main idea of feature screening is reducing the ultra-high dimensionality of the feature space to a moderate size in a fast and efficient way and meanwhile retaining all the important features in the reduced feature space. This is referred to as the sure screening property. After feature screening, more sophisticated methods can be applied to reduced feature space for further analysis such as parameter estimation and statistical inference. This chapter only focuses on the feature screening stage. From the perspective of different types of data, we review feature screening methods for independent and identically distributed data, longitudinal data, and survival data. From the perspective of modeling, we review various models including linear model, generalized linear model, additive model, varying-coefficient model, Cox model, etc. We also cover some model-free feature screening procedures.
AB - This chapter provides a selective review on feature screening methods for ultra-high dimensional data. The main idea of feature screening is reducing the ultra-high dimensionality of the feature space to a moderate size in a fast and efficient way and meanwhile retaining all the important features in the reduced feature space. This is referred to as the sure screening property. After feature screening, more sophisticated methods can be applied to reduced feature space for further analysis such as parameter estimation and statistical inference. This chapter only focuses on the feature screening stage. From the perspective of different types of data, we review feature screening methods for independent and identically distributed data, longitudinal data, and survival data. From the perspective of modeling, we review various models including linear model, generalized linear model, additive model, varying-coefficient model, Cox model, etc. We also cover some model-free feature screening procedures.
UR - http://www.scopus.com/inward/record.url?scp=85076753596&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-31150-6_10
DO - 10.1007/978-3-030-31150-6_10
M3 - Chapter
AN - SCOPUS:85076753596
T3 - Advanced Studies in Theoretical and Applied Econometrics
SP - 293
EP - 326
BT - Advanced Studies in Theoretical and Applied Econometrics
PB - Springer
ER -