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

Climate variability and extremes are expected to increase due to climate change; this may
have significant negative impacts for agricultural production. Previous work has primarily …

This paper applies a bootstrapped Data Envelopment Analysis (DEA) formulation aiming to
evaluate the financial performance of the firms operating in the Greek renewable energy …

We consider bandwidth selection for the kernel estimator of conditional density with one
explanatory variable. Several bandwidth selection methods are derived ranging from fast …

This paper uses the nonparametric DEA methodology to estimate cost and profit efficiency of
Indian banks during the post-reform period. The results show considerable variation in …

Of the various renewable energy resources, wind power is widely recognized as one of the
most promising. The management of wind farms and electricity systems can benefit greatly …

Abstract The Automated Enforcement System (AES) has become the most important traffic
enforcement system in China. In this study, a spatio-temporal kernel density estimation …

This article considers a methodology for flexibly characterizing the relationship between a
response and multiple predictors. Goals are (1) to estimate the conditional response …

We propose a data-driven least-square cross-validation method to optimally select
smoothing parameters for the nonparametric estimation of conditional cumulative …

We extend the idea of crossvalidation to choose the smoothing parameters of the 'double-
kernel'local linear regression for estimating a conditional density. Our selection rule …