Automated Author ProfileZhou, Xianbo
Zhou, Xianbo
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 4.6 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical application to female smoking is provided to illustrate the usefulness of the estimator.
Authors
- Pan, Zhewen ;
- Zhou, Xianbo ;
- Zhou, Yahong
This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical application to female smoking is provided to illustrate the usefulness of the estimator.
Authors
- Pan, Zhewen ;
- Zhou, Xianbo ;
- Zhou, Yahong
This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical application to female smoking is provided to illustrate the usefulness of the estimator.
Authors
- Pan, Zhewen ;
- Zhou, Xianbo ;
- Zhou, Yahong
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Gomez, Laurent ;
- Massari, Mark Eben ;
- Vickers, Troy ;
- Freestone, Graeme ;
- Vernier, William ;
- Ly, Kiev ;
- Xu, Rui ;
- McCarrick, Margaret ;
- Marrone, Tami ;
- Metz, Markus ;
- Yan, Yingzhou G. ;
- Yoder, Zachary W. ;
- Lemus, Robert ;
- Broadbent, Nicola J. ;
- Barido, Richard ;
- Warren, Noelle ;
- Schmelzer, Kara ;
- Neul, David ;
- Lee, Dong ;
- Andersen, Carsten B. ;
- Sebring, Kristen ;
- Aertgeerts, Kathleen ;
- Zhou, Xianbo ;
- Tabatabaei, Ali ;
- Peters, Marco ;
- Breitenbucher, J. Guy