136 research outputs found
Self-Consistency: A Fundamental Concept in Statistics
The term \u27\u27self-consistency\u27\u27 was introduced in 1989 by Hastie and Stuetzle to describe the property that each point on a smooth curve or surface is the mean of all points that project orthogonally onto it. We generalize this concept to self-consistent random vectors: a random vector Y is self-consistent for X if E[X|Y] = Y almost surely. This allows us to construct a unified theoretical basis for principal components, principal curves and surfaces, principal points, principal variables, principal modes of variation and other statistical methods. We provide some general results on self-consistent random variables, give examples, show relationships between the various methods, discuss a related notion of self-consistent estimators and suggest directions for future research
K-Tensors: Clustering Positive Semi-Definite Matrices
This paper introduces a novel self-consistency clustering algorithm
(-Tensors) designed for {partitioning a distribution of}
positive-semidefinite matrices based on their eigenstructures. As positive
semi-definite matrices can be represented as ellipsoids in , , it is critical to maintain their structural information to perform
effective clustering. However, traditional clustering algorithms {applied to
matrices} often {involve vectorization of} the matrices, resulting in a loss of
essential structural information. To address this issue, we propose a distance
metric {for clustering} that is specifically based on the structural
information of positive semi-definite matrices. This distance metric enables
the clustering algorithm to consider the differences between positive
semi-definite matrices and their projections onto {a} common space spanned by
\thadJulyTen{orthonormal vectors defined from a set of} positive semi-definite
matrices. This innovative approach to clustering positive semi-definite
matrices has broad applications in several domains including financial and
biomedical research, such as analyzing functional connectivity data. By
maintaining the structural information of positive semi-definite matrices, our
proposed algorithm promises to cluster the positive semi-definite matrices in a
more meaningful way, thereby facilitating deeper insights into the underlying
data in various applications
Allometric Extension for Multivariate Regression Models
In multivariate regression, interest lies on how the response vector depends on a set of covariates. A multivariate regression model is proposed where the covariates explain variation in the response only in the direction of the first principal component axis. This model is not only parsimonious, but it provides an easy interpretation in allometric growth studies where the first principal component of the log-transformed data corresponds to constants of allometric growth. The proposed model naturally generalizes the two–group allometric extension model to the situation where groups differ according to a set of covariates. A bootstrap test for the model is proposed and a study on plant growth in the Florida Everglades is used to illustrate the model
Self-Consistency Algorithms
The k-means algorithm and the principal curve algorithm are special cases of a self-consistency algorithm. A general self-consistency algorithm is described and results are provided describing the behavior of the algorithm for theoretical distributions, in particular elliptical distributions. The results are used to contrast the behavior of the algorithms when applied to a theoretical model and when applied to finite datasets from the model. The algorithm is also used to determine principal loops for the bivariate normal distribution
Linear Transformations and the k-Means Clustering Algorithm: Applications to Clustering Curves
Functional data can be clustered by plugging estimated regression coefficients from individual curves into the k-means algorithm. Clustering results can differ depending on how the curves are fit to the data. Estimating curves using different sets of basis functions corresponds to different linear transformations of the data. k-means clustering is not invariant to linear transformations of the data. The optimal linear transformation for clustering will stretch the distribution so that the primary direction of variability aligns with actual differences in the clusters. It is shown that clustering the raw data will often give results similar to clustering regression coefficients obtained using an orthogonal design matrix. Clustering functional data using an L(2) metric on function space can be achieved by clustering a suitable linear transformation of the regression coefficients. An example where depressed individuals are treated with an antidepressant is used for illustration
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