Functional linear models are useful in longitudinal data analysis. They include many classical and recently proposed statistical models for longitudinal data and other functional data. Recently, smoothing spline and kernel methods have been proposed for estimating their coe cient functions nonparametrically but these methods are either intensive in computation or ine cient in performance. To ov...

For spline smoothing one can rewrite the smooth estimation as a linear mixed model (LMM) where the smoothing parameter appears as the variance of spline basis coefficients. Smoothing methods that use basis functions with penalization can utilize maximum likelihood (ML) theory in LMM framework ([8]). We introduce the minimum description length (MDL) model selection criterion in LMM and propose a...

We present a novel unified framework for explicitly parameterizing white fiber tracts. The coordinates of tracts are parameterized using a Fourier series expansion. For an arbitrary tract, a 19 degree cosine expansion is found to be sufficient to reconstruct the tract with an error of about 0.26 mm. By adding specific periodic constraints to open tracts, we can avoid using the sine basis. Then ...

The paper introduces a new method for flexible spline fitting for copula density estimation. Spline coefficients are penalized to achieve a smooth fit. To weaken the curse of dimensionality, instead of a full tensor spline basis, a reduced tensor product based on sparse grids Zenger (1991) is used. To achieve uniform margins of the copula density, linear constraints are placed on the spline coe...

Regression analysis is commonly undertaken to identify the effects of each of these characteristics on income (or expenditure) per capita. Attention is needed to choose the independent variables carefully, to be sure that they are indeed exogenous. A number of more exotic techniques are now available for this purpose, including classification and regression tree (CART) models and multiple-adapt...

In truncated polynomial spline or B-spline models where the covariates are measured with error, a fully Bayesian approach to model fitting requires the covariates and model parameters to be sampled at every Markov chain Monte Carlo iteration. Sampling the unobserved covariates poses a major computational problem and usually Gibbs sampling is not possible. This forces the practitioner to use a M...

Interrogative visualization refers to the process of interactive computer graphics display and accurate quantitative querying of physical data. Quantitative querying includes search for metric, combinato-rial and topological information. To support this paradigm, we build uniform, compact, co-registered representations (spline models) of multiple physical data elds over the same domain. Dense, ...