Related papers: A Parameter-Centric View on Regression

Discussion on "Regression by Composition" by Farewell, Daniel, Stensrud, and Huitfeldt.

We discuss the regression-by-composition framework of Farewell, Daniel, Stensrud and Huitfeldt, highlighting a key consequence of its sequential construction: order dependence. Reordering the flows may change the implied conditional…

Beginning with a discussion of R. A. Fisher's early written remarks that relate to dimension reduction, this article revisits principal components as a reductive method in regression, develops several model-based extensions and ends with…

We provide a remedy for two concerns that have dogged the use of principal components in regression: (i) principal components are computed from the predictors alone and do not make apparent use of the response, and (ii) principal components…

In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science amongst…

This paper proposes a new method and algorithm for predicting multivariate responses in a regression setting. Research into classification of High Dimension Low Sample Size (HDLSS) data, in particular microarray data, has made considerable…

Rejoinder to ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Compositional data are common in many fields, both as outcomes and predictor variables. The inventory of models for the case when both the outcome and predictor variables are compositional is limited and the existing models are difficult to…

We propose a supervised principal component regression method for relating functional responses with high dimensional predictors. Unlike the conventional principal component analysis, the proposed method builds on a newly defined expected…

Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies…

Rejoinder on Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with ``Censoring'' Due to Death by D. B. Rubin [math.ST/0612783]

Review of: Brigitte Le Roux and Henry Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Kluwer, Dordrecht, 2004, xi+475 pp.