Additive Isotonic Models
By P. Bacchetti. 1989
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Adityanand Guntuboyina
- Richard J. Samworth
- Yining Chen
- Bodhisattva Sen
- Junming Yin
- Qiyang Han
- Sabyasachi Chatterjee
- Tengyao Wang
Citations in the corpus
- Convex-constrained Sparse Additive Modeling and Its Extensions (2017, methodology): “[Bac89] developed a backfitting (i.e., block coordinate descent) algorithm for solving (3) while ignoring the centering constraint z j ∈ H : in each step, all components except for one z j are fixed, and the well-known pool-adjacent-violators (PAV) algorithm [BBBB72] is applied to optimize z j under…”
- Nonparametric Shape-Restricted Regression (2017, background): “, [6, 35, 95, 103] for a study of some possible applications, identifiability and estimation in such a model.”
- Generalized additive and index models with shape constraints (2014, background): “See, for instance, Brunk (1958, 1970) and Barlow et al. (1972). For the problem of univariate convex regression, see Hanson and Pledger (1976), Groeneboom et al. (2001, 2008) and Guntuboyina and Sen (2013). These references cover consistency, local and global rates of convergence, and computational ”
- LASSO Isotone for High-Dimensional Additive Isotonic Regression (2010, methodology): “In particular, Bacchetti (1989) suggested the additive isotonic model. With the additive isotonic model, we are interested in tackling the problem of conducting regression under the restriction that the regression function is of a prespecified monotonicity with respect to each covariate. Such restri”
- Bayesian Semiparametric Isotonic Regression for Count Data (2005, unknown): “In addition, additive isotonic regression models for multiple predictors have been considered by Bacchetti (1989) and Morton-Jones, Diggle, Parker, Dickinson, and Binks (2000). Schell and Singh (1997) proposed a reduced monotonic regression method for estimating the number and location of change poi”
- Isotonic regression in general dimensions (2017, background): “A separate line of work has generalised the univariate isotonic regression problem to multivariate settings by assuming an additive structure (see e.g. Bacchetti (1989); MortonJones et al. (2000); Mammen and Yu (2007); Chen and Samworth (2016)).”