Ditemukan 12 dokumen yang sesuai dengan query
Sen, Pranab Kumar, author
New York: John Wiley & Sons, 1981
519.4 SEN s
Buku Teks Universitas Indonesia Library
Randles, Ronald H., author
New York: John Wiley & Sons, 1979
519.53 RAN i
Buku Teks Universitas Indonesia Library
Conover, W.J., author
New York: John Wiley & Sons, 1980
519.53 CON p
Buku Teks Universitas Indonesia Library
Conover, W.J., author
New York: John Wiley & Sons, 1999
519.53 CON p
Buku Teks Universitas Indonesia Library
Sen, Pranab Kumar, author
A study of sequential nonparametric methods emphasizing the unified Martingale approach to the theory, with a detailed explanation of major applications including problems arising in clinical trials, life-testing experimentation, survival analysis, classical sequential analysis and other areas of applied statistics and biostatistics...
Philadelphia: Society for Industrial and Applied Mathematics, 1985
e20451175
eBooks Universitas Indonesia Library
Siegel, Sidney, author
New York: McGraw-Hill , 1988
519.5 SIE n
Buku Teks Universitas Indonesia Library
Rayner, J. C. W., author
Boca Raton: Chapman & Hall , 2001
519.5 RAY c
Buku Teks Universitas Indonesia Library
Desu, M.M., author
Boca Raton: Chapman & Hall , 2004
519.5 DES n
Buku Teks Universitas Indonesia Library
Thompson, James R., author
Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for...
Philadelphia : Society for Industrial and Applied Mathematics, 1990
e20442929
eBooks Universitas Indonesia Library
Lee, Herbert K.H., author
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This...
Philadelphia: Society for Industrial and Applied Mathematics, 2004
e20448023
eBooks Universitas Indonesia Library
