- Referenced in 1317 articles
- functional data analysis functions and sample analyses through the CRAN distribution system. This ... files containing the functions and sample analyses, as well as two .txt files giving instructions...
- Referenced in 661 articles
- TSPLIB is a library of sample instances for the TSP (and related problem) from various...
- Referenced in 386 articles
- about the true structure in the large sample limit, provided that structure and the sample...
- Referenced in 202 articles
- Iterative signal recovery from incomplete and inaccurate samples. Compressive sampling offers a new paradigm ... basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from ... noisy samples. This paper describes a new iterative recovery algorithm called CoSaMP that delivers ... requires only matrix-vector multiplies with the sampling matrix. For compressible signals, the running time...
- Referenced in 358 articles
- BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian...
- Referenced in 335 articles
- surface and improving the approximation by sampling where the prediction error may be high...
- Referenced in 273 articles
- conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with...
- Referenced in 265 articles
- intended usage. Please read our sample license agreement (or the german version) for more details...
- Referenced in 125 articles
- SMOTE: Synthetic Minority Over-sampling Technique. An approach to the construction of classifiers from imbalanced ... cost of the reverse error. Under-sampling of the majority (normal) class has been proposed ... combination of our method of over-sampling the minority (abnormal) class and under-sampling ... performance (in ROC space) than only under-sampling the majority class. This paper also shows...
- Referenced in 174 articles
- Stan: A C++ Library for Probability and Sampling. Stan is a probabilistic programming language implementing ... full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with...
- Referenced in 220 articles
- Allows for witness set manipulation via both sampling and membership testing. Accepts square or nonsquare...
- Referenced in 189 articles
- effects Models in S .Data sets and sample analyses from Pinheiro and Bates, ”Mixed-effects...
- Referenced in 130 articles
- some of the algorithms it incorporates. Four sample CAYLEY programs are given: a test ... easy and comfortable to use. As the sample programs show, novel approaches may be needed...
- Referenced in 123 articles
- have to be done. Note, that all sample codes given in [R. Klatte ... work properly with C-XSC 2.0. Sample codes are available on the web page...
- Referenced in 157 articles
- penalised likelihood , survival analysis: descriptive statistics, two-sample tests, parametric accelerated failure models, Cox model...
- Referenced in 102 articles
- evolution strategy, new candidate solutions are sampled according to a multivariate normal distribution ... candidate solutions is exploited for learning the sample distribution and neither derivatives nor even...
- Referenced in 132 articles
- package ’nacopula’ for nested Archimedean copulas: Efficient sampling algorithms, various estimators, goodness-of-fit tests...
- Referenced in 129 articles
- exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types...
- Referenced in 128 articles
- because low discrepancy sequences tend to sample space ”more uniformly” than random numbers. Algorithms that...
- Referenced in 125 articles
- Gray (1988), A class of K-sample tests for comparing the cumulative incidence...