scalation / fda Goto Github PK
View Code? Open in Web Editor NEWFunctional Data Analysis Group
License: MIT License
Functional Data Analysis Group
License: MIT License
We should implement and test Generalized Bayesian Information Criterion (GBIC) [1] for estimating the optimal roughness penalty in the Smoothing_F
class.
[1] Konishi, Sadanori, Tomohiro Ando, and Seiya Imoto. 2004. “Bayesian Information Criteria and Smoothing Parameter Selection in Radial Basis Function Networks.” Biometrika 91 (1). Oxford University Press: 27–43. http://www.jstor.org/stable/20441077
We need to implement and test Generalized Cross Validation (GCV) [1, 2] for estimating the optimal roughness penalty in the Smoothing_F
class.
We need to implement and test Tight Clustering [1].
[1] Tseng, George C., and Wing H. Wong. 2005. “Tight Clustering: A Resampling-Based Approach for Identifying Stable and Tight Patterns in Data.” Biometrics 61 (1). Blackwell Publishing: 10–16. doi:10.1111/j.0006-341X.2005.031032.x.
Need to write instructions/documentation for the software (I've included information in a related email to help get you started) and include these instructions in the README.md
file for the package.
To make things easier, we'll use the https://github.com/scalation/fda/tree/ftclust branch for code/instructions/etc related to the paper.
This issue is for discussion surround the fitting of differential equations to functional data, commonly referred to as Principal Differential Analysis (PDA) [1].
[1] Ramsay, J. O., and B. W. Silverman. 2005. Chapter 19. Functional Data Analysis. 2nd ed. Springer Series in Statistics. New York: Springer-Verlag. doi:10.1007/b98888.
We should implement and test Lasso regression [1] that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
[1] Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological) 58 (1). [Royal Statistical Society, Wiley]: 267–88. http://www.jstor.org/stable/2346178
We need to test the Smoothing_F class against a real dataset. Perhaps a comparison with R (or similar) would be good too.
We need to implement the k-means++ method [1] for choosing the initial centroids in k-means.
[1] David Arthur and Sergei Vassilvitskii. 2007. k-means++: the advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (SODA '07). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1027-1035.
The GeneIsoform
sample application is setup so that we can explore #4 on a gene isoform expression time course dataset.
Need to run the software on the simulated data (attached in a related) provided by Xiaoxiao and email the results to Xiaoxiao and myself. This dataset needs to be converted to CSV and included with the package. You may want to explore running this on sapelo if it takes too long to run.
Need to be able to load the gene iso form expression time course dataset provided by Xiaoxiao. This should be loaded and made available in a MatrixD
.
We should implement and test Modified Akaike Information Criterion (mAIC) [1] for estimating the optimal roughness penalty in the Smoothing_F
class.
[1] Fujikoshi, Y. 1997. “Modified AIC and Cp in Multivariate Linear Regression.” Biometrika 84 (3). Oxford University Press: 707–16. doi:10.1093/biomet/84.3.707.
We should implement and test Generalized Information Criterion (GIC) [1] for estimating the optimal roughness penalty in the Smoothing_F
class.
[1] Konoshi, Sadanori, and Genshiro Kitagawa. 1996. “Generalised Information Criteria in Model Selection.” Biometrika 83 (4). Oxford University Press: 875–90. https://www.jstor.org/stable/2337290
After almost 30 minutes of waiting for the compilation process to finish, I got the following error:
qjava.lang.OutOfMemoryError: GC overhead limit exceeded
at scala.tools.nsc.backend.jvm.analysis.AliasingFrame.(AliasingFrame.scala:43)
We need to implement and test the Gap statistic [1] for choosing the optimal k value (number of clusters) in K-Means.
[1] Tibshirani, Robert, Guenther Walther, and Trevor Hastie. 2001. “Estimating the Number of Clusters in a Data Set via the Gap Statistic.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63 (2). Blackwell Publishers Ltd.: 411–23. doi:10.1111/1467-9868.00293.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.