Change point detection has been recognized as an essential component of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of disturbance in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in an in-control clinical dichotomous process in the presence ...

Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market and foreign exchange rates. This highly persistent, infinite variance—but still mean reverting—behavior is commonly found with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a fully parametric Bayesian estimator, rob...

Extreme rainfall is one of the most devastating natural events. The frequency and intensity of these events has increased. This trend will likely continue as the effects of climate change become more pronounced. As a consequence, it is necessary to evaluate the different statistical methods that assess the occurrence of the extreme rainfalls. This research evaluates some of the most important s...

In this paper, a nonparametric Bayesian estimator in the wavelet domains is presented. In this approach, we propose a prior statistical model based on the a-stable densities adapted to capture the sparseness of the wavelet detail coefficients. An attempt to apply this model in the context of wavelet denoising have been already proposed in (Achim, A., Bezerianos, A., Tsakalides, P., 2001. Novel ...

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and 9 monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of 10 loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability 11 and environmental factors, and cannot be determined through analytical means alon...

Reduced alertness and high levels of cognitive fatigue due to sleep loss bring forth substantial risks in today’s 24/7 society. Biomathematical models can be used to help mitigate such risks by predicting quantitative levels of fatigue under sleep loss. These models help manage risk by providing information on the timing at which high levels of fatigue will occur; countermeasures can then be ta...

We describe and evaluate a new estimator of the effective population size (N(e)), a critical parameter in evolutionary and conservation biology. This new "SummStat" N(e) estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N(e). Simulations of a Wright-Fisher population with known N(e) show that the SummStat estimator is useful across a...

A recently proposed Bayesian modeling framework for classification facilitates both the analysis and optimization of error estimation performance. The Bayesian error estimator is then defined to have optimal mean-square error performance, but in many situations closed-form representations are unavailable and approximations may not be feasible. To address this, we present a method to optimally c...

The paper investigates adaptive equalization of timedispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator...

Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can be used in a variety of applications, it becomes theoretically hard to solve when the number of di...