Principal component estimation of functional logistic regression: discussion of two different approaches

By Manuel Escabias, Ana M. Aguilera, Mariano J. Valderrama. 2004

144 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a4699a018b305ed9acb4a2daf9fe90a93d5c0d02

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In Management Science and Operations Research

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Citations in the corpus

Single and multiple index functional regression models with nonparametric link (2011, background): “Advances in this direction include those made in [1, 9, 10, 14] and [17], where both parametric and nonparametric link functions were introduced in order to connect the response to a linear functional model in the explanatory variables.”
Continuously additive models for nonlinear functional regression (2013, methodology): “To relate generalized responses to functional predictors, the generalized functional linear model (James 2002; Escabias et al. 2004; Cardot & Sarda 2005; Müller & Stadtmüller 2005; Reiss & Ogden 2010) is a common tool and is subject to similar limitations.”
Functional Additive Models (2008, background): “These include generalized functional linear models (James, 2002; Escabias et al., 2004; Cardot and Sarda, 2005; Müller and Stadtmüller, 2005), modifications of functional regression for longitudinal, i.”
Modeling environmental data by functional principal component logistic regression (2005, methodology): “…of PCs of the explicative variables and then to reconstruct the original parameters in terms of the ones given by the model in terms of the PCs. Escabias et al. (2004) propose two different functional principal component approaches to improve the estimation of the parameter function in the FLR…”
Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models (2011, methodology): “…single-iteration methods have been proposed for REML or ML estimation of semiparametric GLMs (e.g. Wood (2004), using Breslow and Clayton (1993), or Fahrmeir et al. (2004), using Harville (1977)), and in practice convergence problems are not unusual: examples are provided in Wood (2004, 2008), and…”
Using basis expansions for estimating functional PLS regression Applications with chemometric data (2010, methodology): “Furthermore, two different FPCA approaches have been developed for estimating the functional logistic regression model [10].”
Two Samples Tests for Functional Data (2010, unknown): “, 1999), generalized linear models with functional predictors (James, 2002), principal component estimation of functional logistic regression (Escabias et al., 2004), and generalized functional linear models (Müller and Stadmüller, 2005) were also studied.”