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010   $a981-10-0889-2
024 7 $a10.1007/978-981-10-0889-4
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035   $a(DE-He213)978-981-10-0889-4
035   $a(MiAaPQ)EBC6310530
035   $a(MiAaPQ)EBC5555637
035   $a(Au-PeEL)EBL5555637
035   $a(OCoLC)953613210
035   $a(PPN)194075672
035   $a(EXLCZ)993710000000717756
100   $a20160520d2016      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aExamples in Parametric Inference with R /$fby Ulhas Jayram Dixit
205   $a1st ed. 2016.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2016.
215   $a1 online resource (LVIII, 423 p. 26 illus.)
311 08$a981-10-0888-4 
320   $aIncludes bibliographical references.
327   $aPrerequisite -- Chapter 1. Sufficiency and Completeness -- Chapter 2. Unbiased Estimation -- Chapter 3. Moment and Maximum Likelihood Estimators -- Chapter 4. Bound for the Variance -- Chapter 5. Consistent Estimator -- Chapter 6. Bayes Estimator -- Chapter 7. Most Powerful Test -- Chapter 8. Unbiased and Other Tests -- Bibliography.
330   $aThis book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.
606   $aStatistics
606   $aMathematical statistics$xData processing
606   $aComputer science$xMathematics
606   $aMathematical statistics
606   $aStatistical Theory and Methods
606   $aStatistics and Computing
606   $aProbability and Statistics in Computer Science
615  0$aStatistics.
615  0$aMathematical statistics$xData processing.
615  0$aComputer science$xMathematics.
615  0$aMathematical statistics.
615 14$aStatistical Theory and Methods.
615 24$aStatistics and Computing.
615 24$aProbability and Statistics in Computer Science.
676   $a519.5
700   $aDixit$b Ulhas Jayram$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755913
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254074603321
996   $aExamples in parametric inference with R$91523316
997   $aUNINA