Hazard regression, Advanced Statistics

Hazard regression is the procedure for modeling the hazard function which does not depend on the suppositions made in Cox's proportional hazards model, namely that the log-hazard function is the additive function of time and the vector of covariates both. In this approach, spline functions are frequently used to model the log-hazard function.

Posted Date: 7/28/2012 7:03:21 AM | Location : United States







Related Discussions:- Hazard regression, Assignment Help, Ask Question on Hazard regression, Get Answer, Expert's Help, Hazard regression Discussions

Write discussion on Hazard regression
Your posts are moderated
Related Questions
A directed graph is simple if each ordered pair of vertices is the head and tail of at most one edge; one loop may be present at each vertex. For each n ≥ 1, prove or disprove the

Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting

It is the art of attempting to exchange something quite small and certain, for something which are large and uncertain. Gambling is big business; in the US, for instance, it is at

Item-total correlation is an  extensively used method for checking the homogeneity of the scale made up of number of items. It is simply the Pearson's product moment correlation c

Wilcoxon's ranksum test is the distribution free method or technique used as an alternative to the Student's t-test for assessing whether two populations have the same location. G

3. a. A researcher in Hong Kong computes the correlation between the percentage of employee turnover and the local unemployment rate (also expressed as a percentage) over a 20-mont

facts and statistics about daycare

VIF is the abbreviation of variance inflation factor which is a measure of the amount of multicollinearity that exists in a set of multiple regression variables. *The VIF value

The time series for RESI1, HI1 and COOK1 have appeared again with different outlier values even though the 17 outliers found early were removed.

A comprehensive regression analysis of the case study London has been carried out to test the 4 assumptions of regression: 1. Variables are normally distributed 2. Linear rel