Mathematical statistics : basic ideas and selected topics / Peter. J. Bickel and Kjell A. Doksum.

By: Contributor(s): Material type: TextTextSeries: Texts in statistical sciencePublication details: Boca Raton : CRC Press, c2015-<c2016>Edition: 2nd edDescription: v. <1-2> : ill. ; 26 cmISBN:
  • 9781498723800 (acidfree paper : v. 1)
  • 9781498722681 (acid-free paper : v. 2)
  • 1498723802
Subject(s): DDC classification:
  • 519.5 BIC 22
LOC classification:
  • QA276 .B47 2015
Summary: This book presents important statistical concepts, methods, and tools not covered in the authors' previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis. It covers a number of topics that are important in current measure theory and practice. It emphasizes nonparametric methods which can really only be implemented with modern computing power on large and complex data sets.
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Holdings
Item type Current library Call number Vol info Status Date due Barcode Item holds
Book Book Calcutta 519.5 BIC (Browse shelf(Opens below)) V2 Available IIMC-0144970
Total holds: 0

"A Chapman & Hall book."

Vol. 2 has no edition statement.

Includes bibliographical references and index.

This book presents important statistical concepts, methods, and tools not covered in the authors' previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis. It covers a number of topics that are important in current measure theory and practice. It emphasizes nonparametric methods which can really only be implemented with modern computing power on large and complex data sets.

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