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 © …

Figure 1.1 provides a prototype for the type of data that we shall consider. It shows the
heights of 10 girls measured at a set of 31 ages in the Berkeley Growth Study (Tuddenham …

Semiparametric regression is concerned with the flexible incorporation of non-linear
functional relationships in regression analyses. Any application area that benefits from …

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

Abstract Fan & Li (2001) propose a family of variable selection methods via penalized
likelihood using concave penalty functions. The nonconcave penalized likelihood estimators …

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

We consider quantile autoregression (QAR) models in which the autoregressive coefficients
can be expressed as monotone functions of a single, scalar random variable. The models …

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 …

Varying-coefficient partially linear models are frequently used in statistical modelling, but
their estimation and inference have not been systematically studied. This paper proposes a …