000			nam a22 4500
999			_c32869 _d32869
008			240216b xxu||||| |||| 00| 0 eng d
020			_a9780367194239
082			_a519.50285 _bZWA
100			_aZwanzig, Silvelyn
245			_aComputer intensive methods in statistics
260			_bCRC Press, _aBoca Raton : _c2020
300			_axiii, 212 p. ; _bill., _c23 cm.
365			_b52.99 _c₤ _d110.20
504			_aIncludes bibliographical references and index.
520			_aThis textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners. Features Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.
650			_aMathematical statistics
650			_aTrial distribution
650			_aSIMEX
650			_aRandom numbers
650			_aNormal distribution
650			_aMarkov chain
650			_aMonte Carlo methods
650			_aImportance sampling
650			_akernel density estimator
650			_aCauchy distribution
650			_aConfidence interval
650			_aF-statistic
650			_aEmpirical distribution function
650			_aEM algorithm
650			_aData processing
650			_aRandom variable generation
700			_aMahjani, Behrang
942			_2ddc _cBK