Typescript. Includes bibliographical references (p. 57).

Typescript. Includes bibliographical references (leaves 71-74).

Thesis (M.S.)--Ohio University, March, 2006. / Title from PDF t.p. Includes bibliographical references (p. 47-48)

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 71-73).

Thesis (Ph.D)--Washington State University, August 2006. / Includes bibliographical references (p. 91-93).

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xvi, 264 p.; also includes graphics. Includes bibliographical references (p. 257-264). Available online via OhioLINK's ETD Center

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 108 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 105-108).

No description available.

No description available.

Multiway data are becoming more and more common. While there are many approaches to extending principal component analysis (PCA) from usual data matrices to multiway arrays, their conceptual differences from the usual PCA, and the methodological implications of such differences remain largely unknown. This thesis aims to specifically address these questions. In particular, we clarify the subtle difference between PCA and singular value decomposition (SVD) for multiway data, and show that multiway principal components (PCs) can be estimated reliably in absence of the eigengaps required by the usual PCA, and in general much more efficiently than the usual PCs. Furthermore, the sample multiway PCs are asymptotically independent and hence allow for separate and more accurate inferences about the population PCs. The practical merits of multiway PCA are further demonstrated through numerical, both simulated and real data, examples.