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Graphics processing units in acceleration of bandwidth selection for kernel density estimation


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Andrzejewski, W., Gramacki, A. and Gramacki, J. (2013). Density estimations for approximate query processing on SIMD architectures, Technical Report RA 03/13, Pozna´n University of Technology, Poznan.Search in Google Scholar

Blohsfeld, B., Korus, D. and Seeger, B. (1999). A comparison of selectivity estimators for range queries on metric attributes, Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, Philadelphia, PA, USA, pp. 239-250.Search in Google Scholar

Bochkanov, S. and Bystritsky, V. (2013). ALGLIB, http://www.alglib.net.Search in Google Scholar

Chapman, B., Jost, G. and van der Pas, R. (2007). Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation), MIT Press, Cambridge, MA.Search in Google Scholar

Duong, T. (2004). Bandwidth Selectors for Multivariate Kernel Density Estimation, Ph.D. thesis, University ofWestern Australia, Perth.Search in Google Scholar

Farooqui, N., Kerr, A., Diamos, G., Yalamanchili, S. and Schwan, K. (2011). A framework for dynamically instrumenting GPU compute applications within GPU Ocelot, Proceedings of the 4th Workshop on General Purpose Processing on Graphics Processing Units, GPGPU-4, Newport Beach, CA, USA, pp. 9:1-9:9.Search in Google Scholar

Gramacki, A., Gramacki, J. and Andrzejewski, W. (2010). Probability density functions for calculating approximate aggregates, Foundations of Computing and Decision Sciences 35(4): 223-240.Search in Google Scholar

Greengard, L. and Strain, J. (1991). The fast Gauss transform, SIAM Journal on Scientific and Statistical Computing 12(1): 79-94.10.1137/0912004Search in Google Scholar

Harris, M. (2007). Optimizing parallel reduction in CUDA, http://developer.download.nvidia.com/assets/cuda/files/reduction.pdf.Search in Google Scholar

Hendriks, H. and Kim, P. (2003). Consistent and efficient density estimation, in V. Kumar, M.L. Gavrilova, C.J.K. Tan and P. L’Ecuyer (Eds.), Proceedings of the 200310.1007/3-540-44839-X_42Search in Google Scholar

International Conference on Computational Science and Its Applications, ICCSA 2003: Part I, Lecture Notes in Computer Science, Vol. 2667, Springer-Verlag, New York, NY, Berlin/Heidelberg, pp. 388-397.Search in Google Scholar

Johnson, N., Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions, Volume 1, Probability and Statistics, John Wiley & Sons, Inc, New York, NY.Search in Google Scholar

Johnson, N., Kotz, S. and Balakrishnan, N. (1995). Continuous Univariate Distributions, Volume 2, Probability and Statistics, John Wiley & Sons, Inc, New York, NY.Search in Google Scholar

Kulczycki, P. (2005). Kernel Estimators in Systems Analysis, Wydawnictwa Naukowo-Techniczne, Warsaw, (in Polish).Search in Google Scholar

Kulczycki, P. (2008). Kernel estimators in industrial applications, in B. Prasad (Ed.), Studies in Fuzziness and Soft Computing. Soft Computing Applications in Industry, Springer-Verlag, Berlin, pp. 69-91.10.1007/978-3-540-77465-5_4Search in Google Scholar

Kulczycki, P. and Charytanowicz, M. (2010). A complete gradient clustering algorithm formed with kernel estimators, International Journal of Applied Mathematics and Computer Science 20(1): 123-134, DOI: 10.2478/v10006-010-0009-3.10.2478/v10006-010-0009-3Search in Google Scholar

Li, Q. and Racine, J. (2007). Nonparametric Econometrics: Theory and Practice, Princeton University Press, Princeton, NJ.Search in Google Scholar

Łukasik, S. (2007). Parallel computing of kernel density estimates with MPI, in Y. Shi, G.D. van Albada, J. Dongarra and P.M.A. Sloot (Eds.), Computational Science-ICCS 2007, Lecture Notes in Computer Science, Vol. 4489, Springer, Berlin/Heidelberg, pp. 726-734.10.1007/978-3-540-72588-6_120Search in Google Scholar

NVIDIA Corporation (2012). NVIDIA CUDA programming guide, http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf.Search in Google Scholar

NVIDIA Corporation (2013). NVIDIA’s next generation CUDA compute architecture: Kepler GK110, http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf.Search in Google Scholar

Michailidis, P.D. and Margaritis, K.G. (2013). Accelerating kernel density estimation on the GPU using the CUDA framework, Applied Mathematical Sciences 7(30): 1447-1476.10.12988/ams.2013.13133Search in Google Scholar

Nelder, J.A. and Mead, R. (1965). A simplex method for function minimization, The Computer Journal 7(4): 308-313.10.1093/comjnl/7.4.308Search in Google Scholar

Raykar, V. and Duraiswami, R. (2006). Very fast optimal bandwidth selection for univariate kernel density estimation, Technical Report CS-TR-4774/UMIACS-TR-2005-73, University of Maryland, College Park, MD.10.1137/1.9781611972764.53Search in Google Scholar

Raykar, V., Duraiswami, R. and Zhao, L. (2010). Fast computation of kernel estimators, Journal of Computational and Graphical Statistics 19(1): 205-220.10.1198/jcgs.2010.09046Search in Google Scholar

Sawerwain, M. (2012). GPU-based parallel algorithms for transformations of quantum states expressed as vectors and density matrices, in R. Wyrzykowski, J. Dongarra, K. Karczewski and J. Wa´sniewski (Eds.), Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science, Vol. 7203, Springer-Verlag, New York, NY/Berlin/Heidelberg, pp. 215-224.10.1007/978-3-642-31464-3_22Search in Google Scholar

Sheather, S. (2004). Density estimation, Statistical Science 19(4): 588-597.10.1214/088342304000000297Search in Google Scholar

Silverman, B. (1986). Density Estimation for Statistics and Data Analysis, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, London.Search in Google Scholar

Silverman, B.W. (1982). Algorithm AS 176: Kernel density estimation using the fast Fourier transform, Journal of the Royal Statistical Society: Series C (Applied Statistics) 31(1): 93-99.10.2307/2347084Search in Google Scholar

Simonoff, J. (1996). Smoothing Methods in Statistics, Springer Series in Statistics, Springer-Verlag, New York, NY/Berlin/Heidelberg.10.1007/978-1-4612-4026-6Search in Google Scholar

Wand, M. and Jones, M. (1995). Kernel Smoothing, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Chapman&Hall, London.Search in Google Scholar

Xavier, C. and Iyengar, S. (1998). Introduction to Parallel Algorithms, Wiley Series on Parallel and Distributed Computing, Wiley.Search in Google Scholar

Yang, C., Duraiswami, R. and Gumerov, N. (2003). Improved fast Gauss transform, Technical Report CS-TR-4495, University of Maryland, College Park, MD. Search in Google Scholar

ISSN:
1641-876X
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Mathematics, Applied Mathematics