國立虎尾科技大學 |

Bilinear Regression Analysis = An Introduction /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bilinear Regression Analysis/ by Dietrich von Rosen.
其他題名:	An Introduction /
作者:	von Rosen, Dietrich.
面頁冊數:	XIII, 468 p. 42 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics . -
電子資源:	https://doi.org/10.1007/978-3-319-78784-8
ISBN:	9783319787848

Bilinear Regression Analysis = An Introduction /
von Rosen, Dietrich.

Bilinear Regression AnalysisAn Introduction /[electronic resource] :by Dietrich von Rosen. - 1st ed. 2018. - XIII, 468 p. 42 illus.online resource. - Lecture Notes in Statistics,2200930-0325 ;. - Lecture Notes in Statistics,214.

Preface -- Introduction -- The Basic Ideas of Obtaining MLEs: A Known Dispersion -- The Basic Ideas of Obtaining MLEs: Unknown Dispersion -- Basic Properties of Estimators -- Density Approximations -- Residuals -- Testing Hypotheses -- Influential Observations -- Appendices -- Indices.

This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.

ISBN: 9783319787848

Standard No.: 10.1007/978-3-319-78784-8doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Bilinear Regression Analysis = An Introduction /
LDR:02758nam a22003975i 4500 001 987839
003 DE-He213
005 20200630124223.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319787848 $9 978-3-319-78784-8
024 7 $a 10.1007/978-3-319-78784-8 $2 doi
035 $a 978-3-319-78784-8
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a von Rosen, Dietrich. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1280224
245 1 0 $a Bilinear Regression Analysis $h [electronic resource] : $b An Introduction / $c by Dietrich von Rosen.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIII, 468 p. 42 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Statistics, $x 0930-0325 ; $v 220
505 0 $a Preface -- Introduction -- The Basic Ideas of Obtaining MLEs: A Known Dispersion -- The Basic Ideas of Obtaining MLEs: Unknown Dispersion -- Basic Properties of Estimators -- Density Approximations -- Residuals -- Testing Hypotheses -- Influential Observations -- Appendices -- Indices.
520 $a This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.
650 0 $a Statistics . $3 1253516
650 0 $a Matrix theory. $3 1023862
650 0 $a Algebra. $2 gtt $3 579870
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Linear and Multilinear Algebras, Matrix Theory. $3 672090
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319787824
776 0 8 $i Printed edition: $z 9783319787831
830 0 $a Lecture Notes in Statistics, $x 0930-0325 ; $v 214 $3 1261934
856 4 0 $u https://doi.org/10.1007/978-3-319-78784-8
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)