References
- 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.
- 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.
- Bochkanov, S. and Bystritsky, V. (2013). ALGLIB, http://www.alglib.net.
- 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.
- Duong, T. (2004). Bandwidth Selectors for Multivariate Kernel Density Estimation, Ph.D. thesis, University ofWestern Australia, Perth.
- 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.
- 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.
- Greengard, L. and Strain, J. (1991). The fast Gauss transform, SIAM Journal on Scientific and Statistical Computing 12(1): 79-94.
- Harris, M. (2007). Optimizing parallel reduction in CUDA, http://developer.download.nvidia.com/assets/cuda/files/reduction.pdf.
- 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 2003
- 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.
- Johnson, N., Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions, Volume 1, Probability and Statistics, John Wiley & Sons, Inc, New York, NY.
- Johnson, N., Kotz, S. and Balakrishnan, N. (1995). Continuous Univariate Distributions, Volume 2, Probability and Statistics, John Wiley & Sons, Inc, New York, NY.
- Kulczycki, P. (2005). Kernel Estimators in Systems Analysis, Wydawnictwa Naukowo-Techniczne, Warsaw, (in Polish).
- 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.
- 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.
- Li, Q. and Racine, J. (2007). Nonparametric Econometrics: Theory and Practice, Princeton University Press, Princeton, NJ.
- 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.
- NVIDIA Corporation (2012). NVIDIA CUDA programming guide, http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf.
- 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.
- 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.
- Nelder, J.A. and Mead, R. (1965). A simplex method for function minimization, The Computer Journal 7(4): 308-313.
- 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.
- Raykar, V., Duraiswami, R. and Zhao, L. (2010). Fast computation of kernel estimators, Journal of Computational and Graphical Statistics 19(1): 205-220.
- 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.
- Sheather, S. (2004). Density estimation, Statistical Science 19(4): 588-597.
- Silverman, B. (1986). Density Estimation for Statistics and Data Analysis, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, London.
- 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.
- Simonoff, J. (1996). Smoothing Methods in Statistics, Springer Series in Statistics, Springer-Verlag, New York, NY/Berlin/Heidelberg.
- Wand, M. and Jones, M. (1995). Kernel Smoothing, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Chapman&Hall, London.
- Xavier, C. and Iyengar, S. (1998). Introduction to Parallel Algorithms, Wiley Series on Parallel and Distributed Computing, Wiley.
- Yang, C., Duraiswami, R. and Gumerov, N. (2003). Improved fast Gauss transform, Technical Report CS-TR-4495, University of Maryland, College Park, MD.