Store besparelser
Hurtig levering
Gemte
Log ind
0
Kurv
Bøger
Bestsellers
Kommer snart
Nyheder
Spil og hobby
Kurv
  1. Bog
  2. Matematik
  3. Sandsynlighedsregning og statistik

Multivariate Reduced-Rank Regression

- Theory, Methods and Applications
Af: Kun Chen, Raja P. Velu, Gregory C. Reinsel Engelsk Paperback

Multivariate Reduced-Rank Regression

- Theory, Methods and Applications
Af: Kun Chen, Raja P. Velu, Gregory C. Reinsel Engelsk Paperback
Tjek vores konkurrenters priser

This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed.

This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance.

This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.


Vis mere
Tjek vores konkurrenters priser
Normalpris
kr 1.002
Fragt: 39 kr
6 - 8 hverdage
20 kr
Pakkegebyr
God 4 anmeldelser på
Tjek vores konkurrenters priser

This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed.

This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance.

This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.


Vis mere
Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 411
ISBN-13: 9781071627914
Indbinding: Paperback
Udgave:
ISBN-10: 1071627910
Kategori: Sandsynlighedsregning og statistik
Udg. Dato: 1 dec 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer-Verlag New York Inc.
Oplagsdato: 1 dec 2022
Forfatter(e): Kun Chen, Raja P. Velu, Gregory C. Reinsel
Forfatter(e) Kun Chen, Raja P. Velu, Gregory C. Reinsel

Kategori Sandsynlighedsregning og statistik

ISBN-13 9781071627914

Sprog Engelsk

Indbinding Paperback

Sider 411

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 1 dec 2022

Oplagsdato 1 dec 2022

Forlag Springer-Verlag New York Inc.