|Cox proportional hazards regression|
|Empirical cumulative distribution function|
|Histogram based on empirical cumulative distribution function|
|Kernel smoothing function estimate for univariate and bivariate data|
|Maximum likelihood estimates|
|Asymptotic covariance of maximum likelihood estimators|
|Fit probability distribution object to data|
|Open Distribution Fitter app|
This example shows how to find the empirical survivor functions and the parametric survivor functions using the Burr type XII distribution fit to data for two groups.
This example shows how to estimate and plot the cumulative hazard and survivor functions for different groups.
This example shows how to construct a Cox proportional hazards model, and assess the significance of the predictor variables.
This example shows how to convert survival data to counting process form and then construct a Cox proportional hazards model with time-dependent covariates.
Learn about censoring, survival data, and the survivor and hazard functions.
Estimate the empirical hazard, survivor, and cumulative distribution functions.
Adjust survival rate estimates to quantify the effect of predictor variables.