This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing...

This paper considers smooth principle component analysis for high dimensional data with very large dimensional observations p and moderate number of individuals N. Our setting is similar to traditional PCA, but we assume the factors are smooth and design a new approach to estimate them. By...

In this paper, we propose a new class of regime shift models with exible switching mechanism that relies on a nonparametric probability function of the observed thresh- old variables. The proposed models generally embrace traditional threshold models with contaminated threshold variables or...

In this paper, we propose a new class of regime shift models with flexible switching mechanism that relies on a nonparametric probability function of the observed threshold variables. The proposed models generally embrace traditional threshold models with contaminated threshold variables or...

The Financial Risk Meter (FRM) is an established mechanism that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of...

K-means clustering is one of the most widely-used partitioning algorithm in cluster analysis due to its simplicity and computational efficiency, but it may not provide ideal clustering results when applying to data with non-spherically shaped clusters. By considering the asymmetrically weighted...

Estimating equations have found wide popularity recently in parametric problems, yielding consistent estimators with asymptotically valid inferences obtained via the sandwich formula. Motivated by a problem in nutritional epidemiology, we use estimating equations to derive nonparametric...

In many regression applications both the independent and dependent variables are measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different nonparametric techniques, regression splines and kernel...