A Winsorized Adaptive Rank Test for Location when Sampling from Asymmetric Distributions

Tail-Adaptive Location Rank Test for the Generalized Secant Hyperbolic Distribution

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Adaptive Metropolis Sampling with Product Distributions

The Metropolis-Hastings (MH) algorithm is a way to sample a provided target distribution π(x). It works by repeatedly sampling a separate proposal distribution T (x, x) to generate a random walk {x(t)}. We consider a modification of the MH algorithm in which T is dynamically updated during the walk. The update at time t uses the {x(t < t)} to estimate the product distribution that has the least...

A log-rank-type test to compare net survival distributions.

In population-based cancer studies, it is often interesting to compare cancer survival between different populations. However, in such studies, the exact causes of death are often unavailable or unreliable. Net survival methods were developed to overcome this difficulty. Net survival is the survival that would be observed if the disease under study was the only possible cause of death. The Poha...

Sampling Distributions Sampling Distributions

Sampling from Gibbs distributions

Sampling from Gibbs distributions by Eric Joseph Vigoda Doctor of Philosophy in Computer Science University of California at Berkeley Professor Alistair Sinclair, Chair This thesis considers computational questions concerning statistical mechanical models of idealized physical systems. The equilibrium state of the physical system is described by a probability distribution over the allowed con g...