[ABE-L] Seminarios em High Dimensional Data Analysis

[Ronaldo Dias]

Prezados(as)

Mais uma vez convidamos a todos que puderem participar, principalmente
aqueles próximos a Campinas, para nossos seminários.

Seminarios em High Dimensional Data Analysis
Quarta-feira, 5/9
sala 221, IMECC

Discussion Section: Yet another regularization problem!
Coord. Prof. G. Ludwig (IMECC-UNICAMP)

 "Smoothing Parameter and model Selection for General Smooth Models"
From: Simon Wood, Natalya Pia e
Benjamin Säfken, publicado no *Journal of the American Statistical
Association* em 2016.

Abstract: This article discusses a general framework for smoothing
parameter estimation for models with regular likelihoods constructed in
terms of unknown smooth functions of covariates. Gaussian random effects
and parametric terms may also be present. By construction the method is
numerically stable and convergent, and enables smoothing parameter
uncertainty to be quantified. The latter enables us to fix a well known
problem with AIC for such models, thereby improving the range of model
selection tools available. The smooth functions are represented by reduced
rank spline like smoothers, with associated quadratic penalties measuring
function smoothness. Model estimation is by penalized likelihood
maximization, where the smoothing parameters controlling the extent of
penalization are estimated by Laplace approximate marginal likelihood. The
methods cover, for example, generalized additive models for nonexponential
family responses (e.g., beta, ordered categorical, scaled t distribution,
negative binomial and Tweedie distributions), generalized additive models
for location scale and shape (e.g., two stage zero inflation models, and
Gaussian location-scale models), Cox proportional hazards models and
multivariate additive models. The framework reduces the implementation of
new model classes to the coding of some standard derivatives of the
log-likelihood.




-- 
Ronaldo Dias
Professor
Dept. of Statistics-IMECC, UNICAMP
www.ime.unicamp.br/~dias
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