A class of cubic and quintic spline modified collocation methods for the solution of two-point boundary value problems

This paper is concerned with the study of a class of methods for solving second and fourth-order two-point boundary-value problems. The methods under consideration are modifications of the standard cubic and quintic spline collocation techniques, and are derived by making use of recent results con- cerning the a posteriori correction of cubic and quintic interpolating spline

Expanding an abridged life table

A question of interest in the demographic and actuarial fields is the estimation of the age-specific mortality pattern when data are given in age groups. Data can be provided in such a form usually because of systematic fluctuations caused by age heaping. This is a phenomenon usual to vital registrations related to age misstatements, usually preferences of ages ending in multiples five. Several techniques for expanding an abridged life table to a complete one are proposed in the literature. Although many of these techniques are considered accurate and are more or less extensively used, they have never been simultaneously evaluated. This work provides a critical presentation, an evaluation and a comparison of the performance of these techniques. For that purpose, we consider empirical data sets for several populations with reliable analytical documentation. Departing from the complete sets of qx-values, we form the abridged ones. Then we apply each one of the expanding techniques considered to these abridged data sets and finally we compare the results with the corresponding complete empirical values.abridged life table, age-specific mortality pattern, complete life table, expanding method, interpolation, life tables, parametric models, probability of dying, splines

Penalized wavelet monotone regression

In this paper we focus on nonparametric estimation of a constrained regression function using penalized wavelet regression techniques. This results into a convex op- timization problem under linear constraints. Necessary and sufficient conditions for existence of a unique solution are discussed. The estimator is easily obtained via the dual formulation of the optimization problem. In particular we investigate a penalized wavelet monotone regression estimator. We establish the rate of convergence of this estimator, and illustrate its finite sample performance via a simulation study. We also compare its performance with that of a recently proposed constrained estimator. An illustration to some real data is given

On the automated extraction of regression knowledge from databases

The advent of inexpensive, powerful computing systems, together with the increasing amount of available data, conforms one of the greatest challenges for next-century information science. Since it is apparent that much future analysis will be done automatically, a good deal of attention has been paid recently to the implementation of ideas and/or the adaptation of systems originally developed in machine learning and other computer science areas. This interest seems to stem from both the suspicion that traditional techniques are not well-suited for large-scale automation and the success of new algorithmic concepts in difficult optimization problems. In this paper, I discuss a number of issues concerning the automated extraction of regression knowledge from databases. By regression knowledge is meant quantitative knowledge about the relationship between a vector of predictors or independent variables (x) and a scalar response or dependent variable (y). A number of difficulties found in some well-known tools are pointed out, and a flexible framework avoiding many such difficulties is described and advocated. Basic features of a new tool pursuing this direction are reviewed

Spline regression for zero-inflated models

We propose a regression model for count data when the classical generalized linear model approach is too rigid due to a high outcome of zero counts and a nonlinear influence of continuous covariates. Zero-Inflation is applied to take into account the presence of excess zeros with separate link functions for the zero and the nonzero component. Nonlinearity in covariates is captured by spline functions based on B-splines. Our algorithm relies on maximum-likelihood estimation and allows for adaptive box-constrained knots, thus improving the goodness of the spline fit and allowing for detection of sensitivity changepoints. A simulation study substantiates the numerical stability of the algorithm to infer such models. The AIC criterion is shown to serve well for model selection, in particular if nonlinearities are weak such that BIC tends to overly simplistic models. We fit the introduced models to real data of children's dental sanity, linking caries counts with the so-called Body-Mass-Index (BMI) and other socioeconomic factors. This reveals a puzzling nonmonotonic influence of BMI on caries counts which is yet to be explained by clinical experts