Non‐parametric small area estimation using penalized spline regression
By Jean D. Opsomer, Gerda Claeskens, Maria Giovanna Ranalli, Goeran Kauermann, F. J. Breidt. 2008
199 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/93a1220855b4063e33317f04a63aead3ea9d0078
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- M. José Lombardía
- Stefan Sperlich
- Thuan Nguyen
- Sunil Rao
- Natalia Rojas-Perilla
- Li‐Chun Zhang
- Nikos Tzavidis
- Jiming Jiang
Citations in the corpus
- Best Predictive Small Area Estimation (2011, background): “One may also consider nonparametric models (e.g., Opsomer et al. 2008) that are less restrictive than parametric ones.”
- From start to finish: a framework for the production of small area official statistics (2018, background): “…et al., 2008; Sinha and Rao, 2009; Chambers et al., 2014; Fabrizi et al., 2014), models with non-parametric instead of linear signal specification (Opsomer et al., 2008; Ugarte et al., 2009) and models that extend the covariance structure of the model by allowing for spatially correlated domain…”
- Semiparametric inference in generalized mixed effects models (2008, background): “Opsomer et al. (2008) proposed a restricted likelihood ratio test for small area effects and non-linearities with splines, following the ideas of Claeskens (2004).”