Projection pursuit multi-index (PPMI) models

Michael G. Akritas

doi:10.1016/j.spl.2016.03.008

Projection pursuit multi-index (PPMI) models

Michael G. Akritas

Statistics

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

The concept of joint projective directions and the class of projection pursuit multi-index (PPMI) models are introduced. PPMI models are MI models with hierarchically defined directions spanning the central mean subspace, and bridge the gap between PP and MI models.

Original language	English (US)
Pages (from-to)	99-103
Number of pages	5
Journal	Statistics and Probability Letters
Volume	114
DOIs	https://doi.org/10.1016/j.spl.2016.03.008
State	Published - Jul 1 2016

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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title = "Projection pursuit multi-index (PPMI) models",

abstract = "The concept of joint projective directions and the class of projection pursuit multi-index (PPMI) models are introduced. PPMI models are MI models with hierarchically defined directions spanning the central mean subspace, and bridge the gap between PP and MI models.",

author = "Akritas, {Michael G.}",

note = "Funding Information: This research was supported in part by NSF Grant No. DMS-1209059 .",

year = "2016",

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doi = "10.1016/j.spl.2016.03.008",

language = "English (US)",

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AU - Akritas, Michael G.

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AB - The concept of joint projective directions and the class of projection pursuit multi-index (PPMI) models are introduced. PPMI models are MI models with hierarchically defined directions spanning the central mean subspace, and bridge the gap between PP and MI models.

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