000			03904cam a22004577i 4500
001			18217334
003			OSt
005			20210930192434.0
008			140709t20142014nyua b 001 0 eng d
010			_a 2014945364
020			_a9781493915064 (cloth)
020			_a1493915061 (cloth)
020			_z149391507X (eBook)
020			_z9781493915071(eBook)
024	7		_a10.1007/978-1-4939-1507-1 _2doi
035			_a(OCoLC)ocn892043563
040			_aCDX _beng _cCDX _erda _dYDXCP _dOCLCQ _dCIT _dBTCTA _dTEU _dOCLCQ _dOCLCF _dDLC
042			_alccopycat
050	0	0	_aQB149 _b.C48 2014
082	0	0	_a520.72/7 _223
100	1		_aChattopadhyay, Asis Kumar, _eauthor.
245	1	0	_aStatistical methods for astronomical data analysis / _cAsis Kumar Chattopadhyay, Tanuka Chattopadhyay.
264		1	_aNew York : _bSpringer, _c[2014]
264		4	_c♭2014
300			_axiii, 362 pages : _billustrations (some color) ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aSpringer Series in Astrostatistics, _x2199-1030 ; _v3
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction to astrophysics -- Introduction to statistics -- Sources of astronomical data -- Statistical inference -- Advanced regression and its applications with measurement error -- Missing observations and imputation -- Dimension reduction and clustering -- Clustering, classification and data mining -- Time series analysis -- Monte Carlo simulation -- Use of software -- Appendix.
520			_aThis book introduces "Astrostatistics" as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter's coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors.-- _cSource other than Library of Congress.
650		0	_aStatistical astronomy.
650		7	_aStatistical astronomy. _2fast _0(OCoLC)fst01132055
700	1		_aChattopadhyay, Tanuka, _eauthor.
830		0	_aSpringer series in astrostatistics ; _v3.
906			_a7 _bcbc _ccopycat _d2 _eepcn _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c2668 _d2668