Please note, we are currently updating the 2018 Journal Metrics. Sankhya, the Indian Journal of Statistics is the official publication of the Indian Statistical Institute. This quarterly journal publishes research articles in the broad areas of Theoretical Statistics, Applied Statistics, Mathematical Statistics and Probability. Each year the Journal is published in two Series. Series A, published in February and August, primarily covers Mathematical Statistics and Probability. Series B, published in May and November, primarily covers Applied and Interdisciplinary Statistics. In case of overlapping topics, the applied and theoretical flavors of the paper are considered to determine the appropriate series.
Sankhya A
Description
Identifiers
ISSN | 0976-836X |
e-ISSN | 0976-8378 |
DOI | 10.1007/13171.0976-8378 |
Publisher
Springer India
Additional information
Data set: Springer
Articles
We provide a probabilistic and infinitesimal view of how the principal component analysis procedure (PCA) can be generalized to analysis of nonlinear manifold valued data. Starting with the probabilistic PCA interpretation of the Euclidean PCA procedure, we show how PCA can be generalized to manifolds in an intrinsic way that does not resort to linearization of the data space. The underlying probability...
Regression models for size-and-shape analysis are developed, where the model is specified in the Euclidean space of the landmark coordinates. Statistical models in this space (which is known as the top space or ambient space) are often easier for practitioners to understand than alternative models in the quotient space of size-and-shapes. We consider a Bayesian linear size-and-shape regression model...
We propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operators. Then, subsequent applications including a k sample test for equality of covariance, a functional data classifier, and an expectation-maximization style clustering algorithm are derived and tested on both simulated and...