Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition

Name: Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition
Brand: Springer Nature
SKU: 9781441998873
Availability: InStock

Best Price (Coupon Required):
Buy Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition for $72.00 at @ Link.springer.com when you apply the 10% OFF coupon at checkout.
Click “Get Coupon & Buy” to copy the code and unlock the deal.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $79.99 - $79.99

BEST PRICE

Single Product Purchase

$72.00

@ Link.springer.com with extra coupon

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$79.99	$79.99		10% OFF This deals requires coupon	$72.00		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9781441998873

Condition

New

Product Description

Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields.

Available Colors

N/A

Available Sizes

N/A