Functional quasi‐likelihood regression models with smooth random effects
By Jeng-Min Chiou, H. Muller, Jane-ling Wang. 2003
110 citations recorded by the source corpus.
Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/dc40c37450321d23852cfa7818a6509f96a1f4d0
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In Ecology
In Ecology, Evolution, Behavior and Systematics
In Nature and Landscape Conservation
Citations in the corpus
- Functional Modelling and Classification of Longitudinal Data * (2005, unknown): “A simple functional response model that has been termed a functional smooth random effects model (Chiou et al., 2003) is EfX ðtÞjZg ¼ lðtÞ þ X1 k¼1 Eðnk jZÞ/kðtÞ; where Z is a possibly multivariate covariate and X is the response function.”
- Penalized spline models for functional principal component analysis (2006, unknown): “Our framework also provides a natural way for examining the time-varying effect of any time-independent random covariate; for example, see Rice and Wu (2000) and Chiou et al. (2003).”
- Linguistic pitch analysis using functional principal component mixed effect models (2010, methodology): “The analysis to find the FPC eigenfunctions φj.t/ follows the methodology that was developed by Chiou et al. (2003).”
- Functional clustering and identifying substructures of longitudinal data (2007, background): “For more details, refer to Chiou et al. (2003) and Yao et al. (2003).”
- Nonparametric Regression Analysis of Longitudinal Data (2005, methodology): “Longitudinal data arise commonly in health sciences and engineering research, but different terms have been applied to describe them.”
- Methodology and convergence rates for functional linear regression (2007, background): “There is a small, but increasing, literature on applications of functional regression to longitudinal data analysis; see, for example, [6, 27].”
- FUNCTIONAL RESPONSE MODELS (2004, methodology): “(6) We note that model (6) reduces to the functional quasi-likelihood regression model of Chiou et al. (2003a) when μ(t, z) = μ0(t), and to the multiplicative effects model of Chiou et al.”
- Functional Data Analysis (2016, methodology): “One could conduct a pooled FPCA combining all data as a first step and then to use the FPCA scores obtained from the first stage to model covariate effects through a single-index model at each FPCA component (Chiou, M¨uller & Wang 2003).”
- Properties of design-based functional principal components analysis (2008, background): “Adapting for instance the single index model (Chiou et al. 2003) or the additive model (Müller and Fang, 2008) on the principal components scores in this survey context would allow us to consider model assisted and small domain estimation in a functional context.”
- Functional linear regression analysis for longitudinal data (2005, methodology): “Following Yao, Müller and Wang [31], the best prediction of the scores ζ∗m is then obtained through the best linear prediction, given the observations Ũ∗ = (U∗1 , . . . ,U ∗ L∗), and the number and locations of these observations, L ∗ and S∗ = (S∗1 , . . . , S ∗ L∗) T .”
- Smoothing splines estimators for functional linear regression (2009, background): “Our setup is inspired by other works dealing with noisy observations of functional data (e.g., Cardot [3] or Chiou, Müller and Wang [9]): At each point tj the corresponding functional value Xi(tj) is corrupted by some random error δij so that actual observations Wi(tj) are given by Wi(tj) = Xi(tj) ”