ON CONFIDENCE INTERVALS FOR GENERALIZED ADDITIVE MODELS BASED ON PENALIZED REGRESSION SPLINES

207 citations recorded by the source corpus.

How researchers see this paper

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

Econometric modelling of the regional knowledge production function in Europe (2015, methodology): “…do not adopt neither a within nor a first difference transformation and estimate the level equation.10 For very low levels of regional R&D expenditure, the patent-R&D relationship is flat, suggesting that a minimum level of R&D in necessary 9 We follow Wood (2006a) and use an approximate F-test.”
Modeling Spatiotemporal Forest Health Monitoring Data (2009, methodology): “In comparison, frequentist confidence intervals are not as straightforward (see Wood 2006a, p. 189).”
Twenty years of P-splines (2015, methodology): “See Wood (2006a) for advice on stable computation.”
Shape constrained additive models (2015, methodology): “We propose shape constrained P-splines (SCOP-splines), based on a novel mildly non linear extension of the Psplines of Eilers and Marx (1996), with novel discrete penalties.”
Statistical procedures for developing earthquake damage fragility curves (2015, background): “These methods are more complex and require more background but can often provide very good results [34, 35].”