With the advance of modern technology, more and more data are being recorded
continuously during a time interval or intermittently at several discrete time points. These are …

Varying coefficient regression models are known to be very useful tools for analysing the
relation between a response and a group of covariates. Their structure and interpretability …

Learning models with uniform performance via distributionally robust optimization Page 1 The
Annals of Statistics 2021, Vol. 49, No. 3, 1378–1406 https://doi.org/10.1214/20-AOS2004 © …

It is generally accepted that climate change is having a negative impact on food security.
However, most of the literature variously focuses on the complex and many mechanisms …

We propose a new method for approximate Bayesian statistical inference on the basis of
summary statistics. The method is suited to complex problems that arise in population …

We consider the problem of predicting a response variable from a set of covariates on a data
set that differs in distribution from the training data. Causal parameters are optimal in terms …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

Searching for an effective dimension reduction space is an important problem in regression,
especially for high dimensional data. We propose an adaptive approach based on …

The majority of empirical research in economics ignores the potential benefits of
nonparametric methods, while the majority of advances in nonparametric theory ignores the …

The varying coefficient models are very important tool to explore the dynamic pattern in
many scientific areas, such as economics, finance, politics, epidemiology, medical science …