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Spline Regression (spline + regression)
Selected AbstractsPenalized spline models for functional principal component analysisJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2006Fang Yao Summary., We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows straightforward incorporation of covariates and simple implementation of approximate inference procedures for coefficients. For the handling of the within-subject correlation, we develop an iterative procedure which reduces the dependence between the repeated measurements that are made for the same subject. The resulting data after iteration are theoretically shown to be asymptotically equivalent (in probability) to a set of independent data. This suggests that the general theory of penalized spline regression that has been developed for independent data can also be applied to functional data. The effectiveness of the proposed procedure is demonstrated via a simulation study and an application to yeast cell cycle gene expression data. [source] Period analysis of variable stars by robust smoothingJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2004Hee-Seok Oh Summary., The objective is to estimate the period and the light curve (or periodic function) of a variable star. Previously, several methods have been proposed to estimate the period of a variable star, but they are inaccurate especially when a data set contains outliers. We use a smoothing spline regression to estimate the light curve given a period and then find the period which minimizes the generalized cross-validation (GCV). The GCV method works well, matching an intensive visual examination of a few hundred stars, but the GCV score is still sensitive to outliers. Handling outliers in an automatic way is important when this method is applied in a ,data mining' context to a vary large star survey. Therefore, we suggest a robust method which minimizes a robust cross-validation criterion induced by a robust smoothing spline regression. Once the period has been determined, a nonparametric method is used to estimate the light curve. A real example and a simulation study suggest that the robust cross-validation and GCV methods are superior to existing methods. [source] Using Penalized Splines to Model Age- and Season-of-Birth-Dependent Effects of Childhood Mortality Risk Factors in Rural Burkina FasoBIOMETRICAL JOURNAL, Issue 1 2009Heiko Becher Abstract Several previous studies have identified risk factors for childhood mortality in high risk areas, such as Sub-Saharan Africa. Among these are lifestyle factors related for example to nutrition or sanitation. Other factors are related to social class, ethnicity and poverty in general. Few studies have investigated a dependence of these factors by age and season of birth which is the focus in this study. We perform a survival analysis of 9121 children born between 1998 and 2001 in a rural area of western Burkina Faso. The whole population is under demographic surveillance since 1993. All cause mortality is used as the endpoint and follow-up information until the age of five years is available. Recently developed spline regression methods are used for the analysis. Ethnic group, religion, age of mother, twin status, sex, and distance to next health center are used as covariates all of which having a clear effect on survival in standard Cox regression analysis. With penalized spline regression, a more detailed risk pattern is observed. Ethnicity is more related to death at early age, as well as age of mother. The effect of the risk factors considered also appear to be related with season of birth (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Convexity and Sheepskin Effects in the Human Capital Earnings Function: Recent Evidence for Filipino MenOXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2 2003Norbert R. Schady The issue of possible non-linearities in the relationship between log wages and schooling has received a good deal of attention in the literature. This paper uses data from a recent, high quality household survey for the Philippines, the 1998 Annual Poverty Indicator Survey (APIS), to test the fit of the log-linear specification for Filipino men. The results are based on a number of estimation strategies, including spline regressions, and semi-parametric regressions with a large number of dummies for years of schooling and experience. The basic conclusions of the paper are two. First, there appear to be large differences between the rates of return to education across levels in the Philippines. In particular, the returns to both primary and secondary education are lower than those for tertiary education, a difference which persists even after correcting for differences in direct private costs across levels. Second, within a given level, the last year of schooling is disproportionately rewarded in terms of higher wages. That is, there are clear sheepskin effects associated with graduation from primary school, secondary school, and university. [source] | ||||||||||