Information-theoretic analysis of multivariate single-cell signaling responses
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pcbi.1007132
Download full text from publisher
References listed on IDEAS
- Tomasz Jetka & Karol Nienałtowski & Sarah Filippi & Michael P. H. Stumpf & Michał Komorowski, 2018. "An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
- Mack, Y. P. & Rosenblatt, M., 1979. "Multivariate k-nearest neighbor density estimates," Journal of Multivariate Analysis, Elsevier, vol. 9(1), pages 1-15, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Chang, Fang & Qiu, Weiliang & Zamar, Ruben H. & Lazarus, Ross & Wang, Xiaogang, 2010. "clues: An R Package for Nonparametric Clustering Based on Local Shrinking," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i04).
- Gery Geenens, 2014. "Probit Transformation for Kernel Density Estimation on the Unit Interval," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 346-358, March.
- Cheng, Philip E., 1995. "A note on strong convergence rates in nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 24(4), pages 357-364, September.
- Penrose, Mathew D., 2000. "Central limit theorems for k-nearest neighbour distances," Stochastic Processes and their Applications, Elsevier, vol. 85(2), pages 295-320, February.
- Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
- Lucio Barabesi, 2001. "Local parametric density estimation methods in line transect sampling," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 22-38.
- Burman, Prabir, 2002. "Estimation of equifrequency histograms," Statistics & Probability Letters, Elsevier, vol. 56(3), pages 227-238, February.
- Wang, Xiaogang & Qiu, Weiliang & Zamar, Ruben H., 2007. "CLUES: A non-parametric clustering method based on local shrinking," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 286-298, September.
- Devroye, Luc & Krzyzak, Adam, 2002. "New Multivariate Product Density Estimators," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 88-110, July.
- Sain, Stephan R., 2002. "Multivariate locally adaptive density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 165-186, April.
- Fan, Yanqin & Hou, Lei & Yan, Karen X., 2018. "On the density estimation of air pollution in Beijing," Economics Letters, Elsevier, vol. 163(C), pages 110-113.
- Dmitri Pavlov & Svetla Slavova & Richard J. Kryscio, 2009. "Estimating Relative Risk on the Line Using Nearest Neighbor Statistics," Methodology and Computing in Applied Probability, Springer, vol. 11(2), pages 249-265, June.
- Zheng Li & Guannan Liu & Qi Li, 2017. "Nonparametric Knn estimation with monotone constraints," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 988-1006, October.
- Kung, Yi-Hung & Lin, Pei-Sheng & Kao, Cheng-Hsiung, 2012. "An optimal k-nearest neighbor for density estimation," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1786-1791.
- Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
- Hino, Hideitsu & Koshijima, Kensuke & Murata, Noboru, 2015. "Non-parametric entropy estimators based on simple linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 72-84.
- Gery Geenens & Arthur Charpentier & Davy Paindaveine, 2014. "Probit Transformation for Nonparametric Kernel Estimation of the Copula Density," Working Papers ECARES ECARES 2014-23, ULB -- Universite Libre de Bruxelles.
- Chu, Ba & Jacho-Chávez, David T., 2012. "k-NEAREST NEIGHBOR ESTIMATION OF INVERSE-DENSITY-WEIGHTED EXPECTATIONS WITH DEPENDENT DATA," Econometric Theory, Cambridge University Press, vol. 28(4), pages 769-803, August.
- Aya-Moreno, Carlos & Geenens, Gery & Penev, Spiridon, 2018. "Shape-preserving wavelet-based multivariate density estimation," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 30-47.
- Devroye, Luc & Krzyzak, Adam, 1999. "On the Hilbert kernel density estimate," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 299-308, September.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007132. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.