Cox Piecewise Constant Hazard Model with Bayesian Method

Exact Bayesian Regression of Piecewise Constant Functions

We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary locations, and levels. The derivation works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propose a Bayesian regression curve as a better way of smoothing data ...

Bayesian Regression of Piecewise Constant Functions

We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary location, and levels. It works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propose a Bayesian regression curve as a better way of smoothing data without blurr...

Detecting multiple change points in piecewise constant hazard functions.

The National Cancer Institute (NCI) suggests a sudden reduction in prostate cancer mortality rates, likely due to highly successful treatments and screening methods for early diagnosis. We are interested in understanding the impact of medical breakthroughs, treatments, or interventions, on the survival experience for a population. For this purpose, estimating the underlying hazard function, wit...

A Streaming Piecewise-Constant Model

The piecewise-constant image model (PWC) is remarkably effective for compressing palette images. This paper discloses a new streaming version of PWC that retains the excellent compression efficiency of the original algorithm while dramatically enhancing compression performance. Further, compression throughput is made more constant, making it possible to code sparse images very quickly.

Bias Of The Cox Model Hazard Ratio