Multi-step ahead forecasting is still an open challenge in time series forecasting. Several
approaches that deal with this complex problem have been proposed in the literature but an …

This paper surveys locally weighted learning, a form of lazy learning and memory-based
learning, and focuses on locally weighted linear regression. The survey discusses distance …

Objective To assess the onset, sequence, and rate of progression of comprehensive
biomarker and clinical measures across the spectrum of Alzheimer disease (AD) using the …

Accurate and robust short-term traffic prediction is an important part of advanced traveler
information systems. With the development of intelligent navigation and autonomous driving …

This chapter discusses the innovations of additional flexible methods for modeling an
individual term in an additive model. It focuses on how we fit additive models. A general and …

Locally weighted regression, or loess, is a way of estimating a regression surface through a
multivariate smoothing procedure, fitting a function of the independent variables locally and …

Nonproportional hazards can often be expressed by extending the Cox model to include
time varying coefficients; eg, for a single covariate, the hazard function for subject i is …

STL is a filtering procedure for decomposing a time series into trend, seasonal, and
remainder components. STL has a simple design that consists of a sequence of applications …

For many types of machine learning algorithms, one can compute the statistically``optimal''
way to select training data. In this paper, we review how optimal data selection techniques …

Research Design and Statistical Analysis provides comprehensive coverage of the design
principles and statistical concepts necessary to make sense of real data. The book's goal is …