Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality.

ABSTRACT:

Background

Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap.

Methods

Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton's method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1.

Results

In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage a

SUBMITTER: Yang L 

PROVIDER: S-EPMC3549928 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature