This article reviews some recent developments on the inference of time series data using the
self-normalized approach. We aim to provide a detailed discussion about the use of self …

While there are various model selection methods, an unanswered but important question is
how to select one of them for data at hand. The difficulty is due to that the targeted behaviors …

Regularized estimation in sparse high-dimensional time series models Page 1 The Annals
of Statistics 2015, Vol. 43, No. 4, 1535–1567 DOI: 10.1214/15-AOS1315 © Institute of …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

While the traditional art market stagnates, the digital art market is booming partially due to its
connection with non-fungible tokens, which allow any unique goods to be mapped in a …

Time series segmentation, which is also known as multiple-change-point detection, is a well-
established problem. However, few solutions have been designed specifically for high …

We show that Google search activity on relevant terms is a strong out-of-sample predictor for
future employment growth in the United States over the period 2004–2019 at both short and …

Consider d dependent change point tests, each based on a CUSUM-statistic. We provide an
asymptotic theory that allows us to deal with the maximum over all test statistics as both the …

In 2015, US consumers returned goods worth $261 billion and the return rates for online
sales sometimes exceeded 30%. Manufacturers and retailers have an interest in predicting …

Supplement to “Change-point detection in panel data via double CUSUM statistic”. We
provide the detailed description of the Local Bootstrap and proof of some auxillary results. In …