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| Gutierrez, Roberto G.; Linhart, Jean Marie; Pitblado, Jeffrey S.. |
| Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (1964) and Watson (1964). Instead of fitting a local mean, one instead fits a local pth-order polynomial. Calculations for local polynomial regression are naturally more complex than those for local means, but local polynomial smooths have better statistical properties. The computational complexity may, however, be alleviated by using a Stata plugin. In this article, we describe the locpoly command for performing local polynomial regression. The calculations involved are implemented in both ado-code and with a plugin, allowing the user to assess the speed improvement obtained from using the plugin. Source code for the plugin is also provided as part of the... |
| Tipo: Journal Article |
Palavras-chave: Local polynomial; Local linear; Smoothing; Kernel; Plugin; Research Methods/ Statistical Methods. |
| Ano: 2003 |
URL: http://purl.umn.edu/116196 |
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| Guan, Weihua; Gutierrez, Roberto G.. |
| With the release of Stata 7, the glm command for fitting generalized linear models underwent a substantial overhaul. Stata 7 glm contains an expanded array of variance estimators, regression diagnostics, and other enhancements. The overhaul took place to coincide with the release of Hardin and Hilbe (2001). With the new glm came a modular design that enables users to program customized link functions, variance functions, and weight functions to be used if Newey-West covariance estimates are desired. Because cases requiring customized link functions are the more prevalent in the literature, only those are considered here. We give two examples where a nonstandard link function is required: the relative survival model of Hakulinen and Tenkanen (1987) and a... |
| Tipo: Journal Article |
Palavras-chave: GLM; Survival analysis; Cox regression; Programming; Research Methods/ Statistical Methods. |
| Ano: 2002 |
URL: http://purl.umn.edu/116023 |
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| Gutierrez, Roberto G.. |
| Frailty models are the survival data analog to regression models, which account for heterogeneity and random effects. A frailty is a latent multiplicative effect on the hazard function and is assumed to have unit mean and variance θ, which is estimated along with the other model parameters. A frailty model is an heterogeneity model where the frailties are assumed to be individual- or spell-specific. A shared frailty model is a random effects model where the frailties are common (or shared) among groups of individuals or spells and are randomly distributed across groups. Parametric frailty models were made available in Stata with the release of Stata 7, while parametric shared frailty models were made available in a recent series of updates. This article... |
| Tipo: Journal Article |
Palavras-chave: Parametric survival analysis; Frailty; Random effects; Overdispersion; Heterogeneity; Research Methods/ Statistical Methods. |
| Ano: 2002 |
URL: http://purl.umn.edu/115948 |
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