# Linear algebra and matrix analysis for statistics / Sudipto Banerjee, Professor of Biostatistics, School of Public Health, University of Minnesota, U.S.A., Anindya Roy, Professor of Statistics, Department of Mathematics and Statistics, University of Maryland, Baltimore County, U.S.A.

##### By: Banerjee, Sudipto [author.]

##### Contributor(s): Roy, Anindya [author.]

Material type: TextSeries: Chapman & Hall/CRC texts in statistical science seriesPublisher: Boca Raton : CRC Press, Taylor & Francis Group, 2014Edition: 1st edDescription: xvii, 565 pages : illustrations ; 25 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781420095388 (hardback); 1420095382 (hardback)Subject(s): Algebras, Linear | Matrices | Mathematical statistics | MATHEMATICS / Algebra / General | MATHEMATICS / Probability & Statistics / GeneralDDC classification: 512.5 LOC classification: QA184.2 | .B36 2014Other classification: MAT002000 | MAT029000 Online resources: Cover image Summary: "Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector-space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic, such as infinite-dimensional spaces, and provides illustrative examples. The authors examine recent developments in diverse fields such as spatial statistics, machine learning, data mining, and social network analysis. Complete in its coverage and accessible to students without prior knowledge of linear algebra, the text also includes results that are useful for traditional statistical applications."--Item type | Current location | Call number | Status | Date due | Barcode |
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Books | Centeral Library Second Floor - Engineering & Architecture | 512.5 B.S.L 2014 (Browse shelf) | Available | 24067 |

Includes bibliographical references (pages 555-557) and index.

"Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector-space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic, such as infinite-dimensional spaces, and provides illustrative examples. The authors examine recent developments in diverse fields such as spatial statistics, machine learning, data mining, and social network analysis. Complete in its coverage and accessible to students without prior knowledge of linear algebra, the text also includes results that are useful for traditional statistical applications."--

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