We present a model for estimation of temperature effects on mortality that is able to capture jointly the typical features of every temperature-death relationship, that is, nonlinearity and delayed effect of cold and heat over a few days. Using a segmented approximation along with a doubly penalized spline-based distributed lag parameterization, estimates and relevant standard errors of the col...

Abstract Penalized spline smoothing is a well-established, nonparametric regression method that efficient for one and two covariates. Its extension to more than covariates straightforward but suffers from exponentially increasing memory demands computational complexity, which brings the its numerical limit. with multiple requires solving large-scale, regularized least-squares problem where occu...

There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated minimizing criteria that approximate the average mean squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a realization of some stochastic process. In this arti...

Truncated power basis expansions and penalized spline methods are demonstrated for estimating nonlinear exposure-response relationships in the Cox proportional hazards model. R code is provided for fitting models to get point and interval estimates. The method is illustrated using a simulated data set under a known exposure-response relationship and in a data application examining risk of carpa...

We study a smoothing spline Poisson regression model for the analysis of mortality data. Being a non-parametric approach it is intrinsically robust, that it is a penalized likelihood estimation method makes available an approximate Bayesian confidence interval and importantly the software gss, its implementation on the freely available statistical package R, makes it easily accessible to the us...

We analyze in a regression setting the link between a scalar response and a functional predictor by means of a Functional Generalized Linear Model. We first give a theoretical framework and then discuss identifiability of the model. The functional coefficient of the model is estimated via penalized likelihood with spline approximation. The L2 rate of convergence of this estimator is given under...

A domain decomposition method for solving large bivariate scattered data fitting problems with bivariate minimal energy, discrete least-squares, and penalized least-squares splines is described. The method is based on splitting the domain into smaller domains, solving the associated smaller fitting problems, and combining the coefficients to get a global fit. Explicit error bounds are establish...

The present study uses penalized splines (p- spline) to estimate the functional relationship between survey variable and auxiliary in a complex design; where population divided into clusters is turn subdivided strata. This has considered case of information at two levels; both cluster element levels. further applied model calibration technique by penalty function total. problems levels have bee...