LEADER 01755cam 2200277 a 450

001 190830

005 20130711084644.0

008 110325s2011 enka b 001 0 eng

010 $a2011013225

020 $a9780470094822 (hbk.)

020 $a0470094826 (hbk.)

020 $a9780470094839 (ebook)

020 $a9780470094846 (ebook)

020 $a9781119977711 (ebook)

020 $a9781119977728 (ebook)

035 $a00229602

050 00$aQA276.8$b.M55 2011

082 00$a519.5/44$222

090 $a519.544/M611///UM046034

100 1 $aMillar, R. B.$q(Russell B.)$3340565

245 10$aMaximum likelihood estimation and inference :$bwith examples in R, SAS and ADMB /$cRussell B. Millar.

260 $aWest Sussex, U.K. :$bWiley/John Wiley & Sons,$c2011.

300 $axvi, 357 p. :$bill. ;$c24 cm.

440 0$aStatistics in practice

504 $aIncludes bibliographical references (p. [337]-344) and index.

520 $a"Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology. The methods are implemented in SAS--the most widely used statistical software package--and the data sets and SAS code are provided on a Web site, enabling the reader to use the methods to solve problems in their own work. This book serves as an ideal text for applied scientists and researchers and graduate students of statistics"--$cProvided by publisher.

520 $a"This book is the first to provide an accessible and practical introduction to likelihood modeling, supported by examples and software, and is suitable for the applied scientist"--$cProvided by publisher.

650 0$aEstimation theory.$3140972

650 0$aChance$xMathematical models.$3340566

856 $uhttp://catalogimages.wiley.com/images/db/jimages/9780470094822.jpg$z電子資源

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