Statistics Toolbox

Statistics Toolbox™ provides statistical and machine learning algorithms and tools for organizing, analyzing, and modeling data. You can use regression or classification for predictive modeling, generate random numbers for Monte Carlo simulations, use statistical plots for exploratory data analysis, and perform hypothesis tests. For analyzing multidimensional data, Statistics Toolbox includes algorithms that let you identify key variables that impact your model with sequential feature selection, transform your data with principal component analysis, apply regularization and shrinkage, or use partial least squares regression. The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-means and hierarchical clustering, k-nearest neighbor search, Gaussian mixtures, and hidden Markov models.

References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Ferger, Dietmar; Venz, John: Density estimation via best (L^2)-approximation on classes of step functions. (2017)
  2. Livni, Haim; Livni, Joseph: Interpretation of findings of founder population genetics studies applying lineage extinction theory (2016)
  3. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  4. Martinez, Wendy L.; Cho, Moon Jung: Statistics in MATLAB: a primer (2015)
  5. Martinez, Wendy L.; Martinez, Angel R.: Computational statistics handbook with MATLAB (2015)
  6. R. Cook; Zhihua Su; Yi Yang: envlp: A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Analysis (2015) not zbMATH
  7. Baron, Michael: Probability and statistics for computer scientists (2014)
  8. Srivastava, Ashok N.: Greener aviation with virtual sensors: a case study (2012) ioport
  9. Janecek, Andreas; Schulze Grotthoff, Stefan; Gansterer, Wilfried N.: LibNMF -- a library for nonnegative matrix factorization (2011)
  10. Pham, Huy Nguyen Anh; Triantaphyllou, Evangelos: A meta-heuristic approach for improving the accuracy in some classification algorithms (2011)
  11. Spiegelman, Clifford H.; Park, Eun Sug; Rilett, Laurence R.: Transportation statistics and microsimulation (2011)
  12. Mitra, Sovan; Date, Paresh: Regime switching volatility calibration by the Baum-Welch method (2010)
  13. Papakostas, G. A.; Boutalis, Y. S.; Karras, D. A.; Mertzios, B. G.: Pattern classification by using improved wavelet compressed Zernike moments (2009)
  14. Kim, Jin Seon; Yum, Bong-Jin: Selection between Weibull and lognormal distributions: a comparative simulation study (2008)
  15. Rutherford, Brian M.: Computational modeling issues and methods for the “regulatory problem” in engineering - solution to the thermal problem (2008)
  16. Baron, Michael: Probability and statistics for computer scientists. (2007)
  17. Korenius, Tuomo; Laurikkala, Jorma; Juhola, Martti; Järvelin, Kalervo: Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments (2006) ioport
  18. Cohen, Joel E.; Newman, Charles M.; Cohen, Adam E.; Petchey, Owen L.; Gonzalez, Andrew: Spectral mimicry: A method of synthesizing matching time series with different Fourier spectra (1999)