The rapid recent progress in machine learning (ML) has raised a number of scientific
questions that challenge the longstanding dogma of the field. One of the most important …

Object oriented data analysis is the statistical analysis of populations of complex objects. In
the special case of functional data analysis, these data objects are curves, where a variety of …

The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

Designing an optimum portfolio that allocates weights to its constituent stocks in a way that
achieves the best trade-off between the return and the risk is a challenging research …

The aim of this paper is to establish several deep theoretical properties of principal
component analysis for multiple-component spike covariance models. Our new results …

PCA is a popular tool for exploring and summarizing multivariate data, especially those
consisting of many variables. PCA, however, is often not simple to interpret, as the …

Rank and factor loadings estimation in time series tensor factor model by pre-averaging
Page 1 The Annals of Statistics 2024, Vol. 52, No. 1, 364–391 https://doi.org/10.1214/23-AOS2350 …

In this paper, we consider clustering based on the kernel principal component analysis
(KPCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons …

The present study aims to assess the spatio-temporal growth of urban sub-centre of North 24
Parganas District of West Bengal, India during 1989–2016. Landsat TM and Landsat 8 OLI …

Peter Hall's work illuminated many aspects of statistical thought, some of which are very well
known including the bootstrap and smoothing. However, he also explored many other lesser …