EXTENDING CORRELATION AND REGRESSION FROM MULTIVARIATE TO FUNCTIONAL DATA

By G. He, H. Müller, J. L. Wang. 2000

79 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Organic Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/57cb1defcb799652f7ebbb57d7b1812c502d4056

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. H. Muller
2. Jane-ling Wang
3. Fang Yao
4. H. ller
5. Christophe Crambes
6. G. He
7. Alois Kneip
8. Pascal Sarda

In Organic Chemistry

1. H. Muller
2. Fang Yao

In Statistics and Probability

1. H. Muller
2. Jane-ling Wang
3. H. ller
4. Fang Yao
5. Christophe Crambes
6. Jeng-Min Chiou
7. Pascal Sarda
8. Tomasz Górecki

Citations in the corpus

• Selected statistical methods of data analysis for multivariate functional data (2018, background): “He et al. (2000) showed that random processes with finite basis expansion have simple canonical structures, analogously to the case of random vectors.”
• Functional Modelling and Classification of Longitudinal Data. (2005, background): “A “Functional Normal Equation” (He et al., 2000) extends the multivariate normal equations, and a solution can be obtained under certain conditions.”
• 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) ”
• Functional canonical analysis for square integrable stochastic processes (2003, background): “The proof for parts (b)–(e) can be found in [7].”
• Functional linear regression analysis for longitudinal data (2005, background): “This representation holds under certain regularity conditions which are outlined in [20] and is given by”
• Functional Additive Models (2008, background): “Plugging (3) into (1)–(2) and observing (A.1) in the Appendix then leads to E(Y”
• Functional Modelling and Classification of Longitudinal Data * (2005, background): “A Functional Normal Equation (He et al., 2000) extends the multivariate normal equations, and a solution can be obtained under certain conditions.”
• Electricity consumption prediction with functional linear regression using spline estimators (2010, background): “First, comparison with other estimation procedures such as the one based on functional principal components [12] could be an interesting task that could be realized through simulations or on real data sets like the one studied here.”