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Varying‐coefficient semiparametric model averaging prediction
Author(s) -
Li Jialiang,
Xia Xiaochao,
Wong Weng Kee,
Nott David
Publication year - 2018
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12904
Subject(s) - nonparametric statistics , covariate , parametric statistics , semiparametric model , semiparametric regression , inference , computer science , data set , parametric model , multivariate statistics , statistical inference , flexibility (engineering) , set (abstract data type) , econometrics , mathematics , statistics , machine learning , artificial intelligence , programming language
Summary Forecasting and predictive inference are fundamental data analysis tasks. Most studies employ parametric approaches making strong assumptions about the data generating process. On the other hand, while nonparametric models are applied, it is sometimes found in situations involving low signal to noise ratios or large numbers of covariates that their performance is unsatisfactory. We propose a new varying‐coefficient semiparametric model averaging prediction (VC‐SMAP) approach to analyze large data sets with abundant covariates. Performance of the procedure is investigated with numerical examples. Even though model averaging has been extensively investigated in the literature, very few authors have considered averaging a set of semiparametric models. Our proposed model averaging approach provides more flexibility than parametric methods, while being more stable and easily implemented than fully multivariate nonparametric varying‐coefficient models. We supply numerical evidence to justify the effectiveness of our methodology.