References
- Abazajian, K. N. Abazajian, K. N., Adelman-McCarthy, J. K., Agueros, M. A., Allam, S. S., Prieto, C. A., An, D., et al. (2009). The seventh data release of the Sloan Digital Sky survey. The Astrophysical Journal Supplement, 182, 543-558. https://doi.org/10.1088/0067-0049/182/2/543
- Al-Jarrah, O. Y., Yoo, P. D., Muhaidat, S., Karagiannidis, G. K., and Taha, K. (2015). Efficient machine learning for big data: a review. Big Data Research, 2, 87-93. https://doi.org/10.1016/j.bdr.2015.04.001
- Allison, R. and Dunkley, J. (2014). Comparison of sampling techniques for Bayesian parameter estimation. Monthly Notices of the Royal Astronomical Society, 437, 3918-3928. https://doi.org/10.1093/mnras/stt2190
- Alonso, D. (2012). CUTE solutions for two-point correlation functions from large cosmological datasets, ArXiv e-prints, 1210.1833. Available from: https://arxiv.org/abs/1210.1833
- Ball, N. M. and Brunner, R. J. (2010). Data mining and machine learning in astronomy. International Journal of Modern Physics D, 19, 1049-1106. https://doi.org/10.1142/S0218271810017160
- Bhat, P. C. (2011). Multivariate analysis methods in particle physics. Annual Review of Nuclear and Particle Science, 61, 281-309. https://doi.org/10.1146/annurev.nucl.012809.104427
- Borne, K. (2013). Virtual observatories, data mining, and astroinformatics. In Planets, Stars and Stellar Systems (pp. 403-443), Springer Netherlands
- Borra, S. and Di Ciaccio, A. (2010). Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Computational Statistics & Data Analysis, 54, 2976-2989. https://doi.org/10.1016/j.csda.2010.03.004
- Cavuoti, S., Brescia, M., De Stefano, V., and Longo, G. (2015). Photometric redshift estimation based on data mining with PhotoRApToR. Experimental Astronomy, 39, 45-71. https://doi.org/10.1007/s10686-015-9443-4
- Chapelle, O., Schlkopf, B., and Zien, A. (2010). Semi-Supervised Learning, The MIT Press.
- Feigelson, E. D. and Babu, J. (2012). Statistical Challenges in Modern Astronomy V, (Volume 902 of Lecture Notes in Statistics), Springer, New York.
- Feroz, F., Hobson, M. P., and Bridges, M. (2009). MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics. Monthly Notices of the Royal Astronomical Society, 398, 1601-1614. https://doi.org/10.1111/j.1365-2966.2009.14548.x
- Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J. (2013). emcee: The MCMC Hammer. Publications of the Astronomical Society of Pacific, 125, 306-312. https://doi.org/10.1086/670067
- Gebru, I. D., Alameda-Pineda, X., Forbes, F., and Horaud, R. (2015). EM algorithms for weighted-data clustering with application to audio-visual scene analysis, CoRR, Available from: https://arxiv.org/abs/1509.01509
- Golombek, D. (2004). Archives, databases and the emerging virtual observatories. Astrophysics and Space Science, 290, 449-456. https://doi.org/10.1023/B:ASTR.0000032543.18493.d6
- Gunn, J. E., Siegmund, W. A., Mannery, E. J., Owen, R. E., Hull, C. L., Leger, R. F., et al. (2006). The 2.5 m telescope of the sloan digital sky survey. The Astronomical Journal, 131, 2332-2359. https://doi.org/10.1086/500975
- Hahm, J., Kwon, O.-K., Kim, S., Jung, Y.-H., Yoon, J.-W., Kim, J., Kim, M.-K., Byun, Y.-I., Shin, M.-S., and Park, C. (2012). Astronomical time series data analysis leveraging science cloud, In Lecture Notes in Electrical Engineering, 181, 493-500.
- Hira, Z. M. and Gillies, D. F. (2015). A review of feature selection and feature extraction methods applied on microarray data, Advances in Bioinformatics, 2015, Article ID 198363.
- Ihaka, R. and Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5, 299-314.
- Ivezic, Z., Tyson, J. A., Abel, B., Acosta, E., Allsman, R., AlSayyad, Y., et al. (2008). LSST: from science drivers to reference design and anticipated data products, ArXiv e-prints, 0805.2366, Available from: https://arxiv.org/abs/0805.2366
- Ivezic, Z., Connolly, A. J., VanderPlas, J. T., and Gray, A. (2014). Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Princeton University Press.
- Liao, K., Treu, T., Marshall, P., Fassnacht, C. D., Rumbaugh, N., Dobler, G., et al. (2015). Strong lens time delay challenge. II. Results of TDC1. The Astrophysical Journal, 800, 11. https://doi.org/10.1088/0004-637X/800/1/11
- Patil, A., Huard, D., and Fonnesbeck, C. (2010). PyMC: Bayesian stochastic modelling in python. Journal of Statistical Software, 35, 4.
- Pier, J. R., Munn, J. A., Hindsley, R. B., Hennessy, G. S., Kent, S. M., Lupton, R. H., et al. (2003). Astrometric calibration of the sloan digital sky survey. The Astronomical Journal, 125, 1559-1579. https://doi.org/10.1086/346138
- Saeys, Y., Inza, I., and Larra-naga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507-2517. https://doi.org/10.1093/bioinformatics/btm344
- Shin, M.-S. and Byun, Y.-I. (2004). Efficient period search for time series photometry. Journal of Korean Astronomical Society, 37, 79-85. https://doi.org/10.5303/JKAS.2004.37.2.079
- Singh, N., Browne, L.-M,. and Butler, R. (2013). Parallel astronomical data processing with Python: Recipes for multicore machines. Astronomy and Computing, 2, 1-10. https://doi.org/10.1016/j.ascom.2013.04.002
- Stetson, P. B. (1996). On the automatic determination of light-curve parameters for Cepheid variables. Publications of the Astronomical Society of the Pacific, 108, 851-876. https://doi.org/10.1086/133808
- Szalay, A. S., Kunszt, P. Z., Thakar, A. R., Gray, J., and Slutz, D. (2000). The sloan digital sky survey and its archive, Astronomical Data Analysis Software and Systems IX. ASP Conference Proceedings, 216, 405-414.
- Szapudi, I., Pan, J., Prunet, S., and Budavari, T. (2005). Fast edge-corrected measurement of the two-point correlation function and the power spectrum. The Astrophysical Journal, 631, L1-L4. https://doi.org/10.1086/496971
- Townsend, R. H. D. (2010). Fast calculation of the Lomb-Scargle periodogram using graphics processing units. The Astrophysical Journal Supplement, 191, 247-253. https://doi.org/10.1088/0067-0049/191/2/247
- Vio, R., Diaz-Trigo, M., and Andreani, P. (2013). Irregular time series in astronomy and the use of the Lomb-Scargle periodogram. Astronomy and Computing, 1, 5-16. https://doi.org/10.1016/j.ascom.2012.12.001
- Way, M. J., Scargle, J. D., Ali, K. M., and Srivastava, A. N. (2012). Advances in Machine Learning and Data Mining for Astronomy (1st ed.), Chapman & Hall/CRC.
- Zhang, Y. and Zhao, Y. (2015). Astronomy in the big data era. Data Science Journal, 14, 1-9.
- Zheng, H. and Zhang, Y. (2008). Feature selection for high-dimensional data in astronomy. Advances in Space Research, 41, 1960-1964. https://doi.org/10.1016/j.asr.2007.08.033
- Zhou, Z.-H. (2015). Ensemble learning, Encyclopedia of Biometrics, Springer US, Boston.
- Zuntz, J., Paterno, M., Jennings, E., Rudd, D., Manzotti, A., Dodelson, S., Bridle, S., Sehrish, S., and Kowalkowski, J. (2015). CosmoSIS: Modular cosmological parameter estimation. Astronomy and Computing, 12, 45-59. https://doi.org/10.1016/j.ascom.2015.05.005
- Von Neumann, J. (1941). Distribution of the ratio of mean square successive difference to the variance. The Annals of Mathematical Statistics, 12, 367-395. https://doi.org/10.1214/aoms/1177731677