Q
Qi Li
Researcher at Ocean University of China
Publications - 2269
Citations - 61781
Qi Li is an academic researcher from Ocean University of China. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 102, co-authored 1563 publications receiving 46762 citations. Previous affiliations of Qi Li include China Academy of Engineering Physics & University of Science and Technology of China.
Papers
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Book
Nonparametric Econometrics : Theory and Practice
Qi Li,Jeffrey S. Racine +1 more
TL;DR: Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems and is the ideal introduction for graduate students and an indispensable resource for researchers.
Journal ArticleDOI
Flexible high-temperature dielectric materials from polymer nanocomposites
Qi Li,Lei Chen,Matthew R. Gadinski,Shihai Zhang,Guangzu Zhang,Haoyu U. Li,Aman Haque,Long Qing Chen,Thomas N. Jackson,Qing Wang +9 more
TL;DR: Crosslinked polymer nanocomposites that contain boron nitride nanosheets have outstanding high-voltage capacitive energy storage capabilities at record temperatures and have been demonstrated to preserve excellent dielectric and capacitive performance after intensive bending cycles, enabling broader applications of organic materials in high-temperature electronics and energy storage devices.
Posted Content
Nonparametric Econometrics: Theory and Practice
Qi Li,Jeffrey S. Racine +1 more
TL;DR: Nonparametric Econometrics as discussed by the authors is an excellent introduction to nonparametric and semiparametric methods for economic analysis. But it does not address the problem of dealing with the presence of discrete variables.
Journal ArticleDOI
Nonparametric estimation of regression functions with both categorical and continuous data
Jeffrey S. Racine,Qi Li +1 more
TL;DR: A method for nonparametric regression which admits continuous and categorical data in a natural manner using the method of kernels is proposed, and the asymptotic normality of the estimator is established.
Journal ArticleDOI
Cross-validation and the estimation of conditional probability densities
TL;DR: This article shows that cross-validation produces asymptotically optimal smoothing for relevant components, while eliminating irrelevant components by oversmoothing in the problem of nonparametric estimation of a conditional density.