Generalized additive models for cancer mapping with incomplete covariates
نویسندگان
چکیده
منابع مشابه
Generalized additive models for cancer mapping with incomplete covariates.
Maps depicting cancer incidence rates have become useful tools in public health research, giving valuable information about the spatial variation in rates of disease. Typically, these maps are generated using count data aggregated over areas such as counties or census blocks. However, with the proliferation of geographic information systems and related databases, it is becoming easier to obtain...
متن کاملBayesian Functional Generalized Additive Models with Sparsely Observed Covariates
The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2012) as a more flexible alternative to the common functional linear model (FLM) for regressing a scalar on functional covariates. In this paper, we develop a Bayesian version of FGAM for the case of Gaussian errors with identity link function. Our approach allows the functional covariates to be sparsely ob...
متن کاملGeneralized Additive Partial Linear Models for Clustered Data with Diverging Number of Covariates Using Gee
We study flexible modeling of clustered data using marginal generalized additive partial linear models with a diverging number of covariates. Generalized estimating equations are used to fit the model with the nonparametric functions being approximated by polynomial splines. We investigate the asymptotic properties in a “large n, diverging p” framework. More specifically, we establish the consi...
متن کاملEstimation and Inference in Generalized Additive Coefficient Models for Nonlinear Interactions with High-Dimensional Covariates.
In the low-dimensional case, the generalized additive coefficient model (GACM) proposed by Xue and Yang [Statist. Sinica16 (2006) 1423-1446] has been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables. In this paper, we propose estimation and inference procedures for the GACM when the dimension of the variables is high. Specifically, we propose a groupwis...
متن کاملAdditive risk models for survival data with high-dimensional covariates.
As a useful alternative to Cox's proportional hazard model, the additive risk model assumes that the hazard function is the sum of the baseline hazard function and the regression function of covariates. This article is concerned with estimation and prediction for the additive risk models with right censored survival data, especially when the dimension of the covariates is comparable to or large...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biostatistics
سال: 2004
ISSN: 1465-4644,1468-4357
DOI: 10.1093/biostatistics/5.2.177