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- Fang YAO, Jane-Ling WANG
- 2004

We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data analysis requires a large number of regularly spaced… (More)

We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for… (More)

- Khusvinder Gill, Shuang-Hua Yang, Fang Yao, Xin Lu
- IEEE Trans. Consumer Electronics
- 2009

In recent years, the home environment has seen a rapid introduction of network enabled digital technology. This technology offers new and exciting opportunities to increase the connectivity of devices within the home for the purpose of home automation. Moreover, with the rapid expansion of the Internet, there is the added potential for the remote control… (More)

- Fang Yao, Hans-Georg Müller, +5 authors John S Vogel
- Biometrics
- 2003

We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce… (More)

In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process… (More)

- Elif F Acar, Radu V Craiu, Fang Yao
- Biometrics
- 2011

The study of dependence between random variables is a mainstay in statistics. In many cases, the strength of dependence between two or more random variables varies according to the values of a measured covariate. We propose inference for this type of variation using a conditional copula model where the copula function belongs to a parametric copula family… (More)

We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows… (More)

In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption and propose to replace it by an additive structure. This… (More)

- Stephan Fahr, Fang Yao
- 2008

We analyze the dynamic e¤ects of lumpy factor adjustments at the
rm level onto the aggregate economy. We
nd that distinguishing between capital and labour as lumpy factors within the production function result in very di¤erent dynamics for aggregate output, investment and labour in an otherwise standard real business cycle model. Lumpy capital leaves the… (More)

- Fang Yao, Zhao Yang Dong, Ke Meng, Zhao Xu, Herbert H. C. Iu, Kit Po Wong
- IEEE Transactions on Industrial Informatics
- 2012

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm… (More)