The DOI matches its Crossref record.
Remarks on Some Nonparametric Estimates of a Density Function
Open accessAbstract
This note discusses some aspects of the estimation of the density function of a univariate probability distribution. All estimates of the density function satisfying relatively mild conditions are shown to be biased. The asymptotic mean square error of a particular class of estimates is evaluated.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
Methods for Summarizing Radiocarbon Datasets
Ramsey, C. B. (2017) · Radiocarbon · doi:10.1017/rdc.2017.108 · Cited by 1,179
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Goldstein, M., & Uchida, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152173 · Cited by 1,032
Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.30 · Cited by 900
Conditional density-based analysis of T cell signaling in single-cell data
Krishnaswamy, S., Spitzer, M. H., Mingueneau, M., Bendall, S. C., Litvin, O., Stone, E., Pe’er, D., & Nolan, G. P. (2014) · Science · doi:10.1126/science.1250689 · Cited by 225
Probability measures on the space of persistence diagrams
Mileyko, Y., Mukherjee, S., & Harer, J. (2011) · Inverse Problems · doi:10.1088/0266-5611/27/12/124007 · Cited by 249
Barua, S., Fang, P., Sharma, A., Fujimoto, J., Wistuba, I., Rao, A. U. K., & Lin, S. H. (2018) · Lung Cancer · doi:10.1016/j.lungcan.2018.01.022 · Cited by 223
Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Zhang, J., Kailkhura, B., & Han, Y.-J. (2020) · Unpublished · doi:10.13140/rg.2.2.11390.13125 · Cited by 86
Active articulation model estimation through interactive perception
Hausman, K., Niekum, S., Osentoski, S., & Sukhatme, G. S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139655 · Cited by 77
Note—Estimating Geographic Customer Densities Using Kernel Density Estimation
Donthu, N., & Rust, R. T. (1989) · Marketing Science · doi:10.1287/mksc.8.2.191 · Cited by 52
A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing
Sarzaeim, P., Mahmoud, Q. H., Azim, A., Bauer, G., & Bowles, I. (2023) · Computers · doi:10.3390/computers12120255 · Cited by 51
Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms
Kokkodis, M., & Lappas, T. (2020) · Information Systems Research · doi:10.1287/isre.2019.0895 · Cited by 48
Proton Transfer in Guanine-Cytosine Base Pairs in B-DNA
Soler-Polo, D., Mendieta-Moreno, J. I., Trabada, D. G., Mendieta, J., & Ortega, J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00757 · Cited by 38
Crowdsourcing the Unknown: The Satellite Search for Genghis Khan
Lin, A. Y.-M., Huynh, A., Lanckriet, G., & Barrington, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114046 · Cited by 35
Time Series Regression and Exploratory Data Analysis
Shumway, R. H., & Stoffer, D. S. (2017) · Springer Texts in Statistics · doi:10.1007/978-3-319-52452-8_2 · Cited by 13
Metadata
- Authors
- Rosenblatt, M.
- Venue
- The Annals of Mathematical Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1956-09
- Volume
- 27
- Issue
- 3
- Pages
- 832-837
- ISSN
- 0003-4851 (ISSN-L)
- OpenAlex
- W2014268383
- Cited by
- 4,547
- References
- 0
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
