Jürgen Pilz

Effectiveness of respiratory-sinus-arrhythmia biofeedback on state-anxiety in patients undergoing coronary angiography

AIM This study is a report of a study conducted to evaluate the value of psychological assistance including respiratory-sinus-arrhythmia biofeedback training in its ability to reduce the level of anxiety in patients undergoing coronary angiography. BACKGROUND Coronary angiography has been reported to cause anxiety and emotional stress. METHODS Between March 2004 and January 2005, 212 patients undergoing routine elective coronary angiography for the evaluation of stable coronary artery disease were randomized into two groups. In the psychological support group (n = 106) a structured psychological conversation and respiratory-sinus-arrhythmia biofeedback training were offered prior to coronary angiography. In the control group (n = 106) standard care and information was provided without psychological support. State-anxiety was measured (scale 20-80) 1 day prior to and after coronary angiography, along with blood pressure and heart rate. FINDINGS Prior to coronary angiography, state-anxiety was 54.8 +/- 11.5 (mean +/- SD) in the control group and 54.8 +/- 12.6 in the psychological support group. After coronary angiography, state-anxiety was 47.9 +/- 18.5 in the control group but 28.3 +/- 12.5 in the psychological support group (Wilcoxon rank sum test W = 7272, P < 0.001). Blood pressure was statistically significantly lower in the psychological support group prior to the intervention and the day after coronary angiography. CONCLUSION Psychological support including respiratory-sinus-arrhythmia biofeedback is an effective and simple tool that could be used by nurses to reduce state-anxiety and emotional stress in patients undergoing coronary angiography.

A new bivariate Gamma distribution generated from functional scale parameter with application to drought data

Univariate and bivariate Gamma distributions are among the most widely used distributions in hydrological statistical modeling and applications. This article presents the construction of a new bivariate Gamma distribution which is generated from the functional scale parameter. The utilization of the proposed bivariate Gamma distribution for drought modeling is described by deriving the exact distribution of the inter-arrival time and the proportion of drought along with their moments, assuming that both the lengths of drought duration (X) and non-drought duration (Y) follow this bivariate Gamma distribution. The model parameters of this distribution are estimated by maximum likelihood method and an objective Bayesian analysis using Jeffreys prior and Markov Chain Monte Carlo method. These methods are applied to a real drought dataset from the State of Colorado, USA.

On the performance of a new bivariate pseudo Pareto distribution with application to drought data

A new bivariate pseudo Pareto distribution is proposed, and its distributional characteristics are investigated. The parameters of this distribution are estimated by the moment-, the maximum likelihood- and the Bayesian method. Point estimators of the parameters are presented for different sample sizes. Asymptotic confidence intervals are constructed and the parameter modeling the dependency between two variables is checked. The performance of the different estimation methods is investigated by using the bootstrap method. A Markov Chain Monte Carlo simulation is conducted to estimate the Bayesian posterior distribution for different sample sizes. For illustrative purposes, a real set of drought data is investigated.

Applying Bayesian mixtures-of-experts models to statistical description of smart power semiconductor reliability

Abstract Reliability prediction of semiconductor devices gains importance, since demand increases and resources, e.g. time, are restricted. Normally, methods focusing on technology aspects are applied. This work presents a more mathematical approach by using Bayesian statistics. Physical failure inspection and past research indicate that the data follow a bimodal distribution. Therefore, we suggest using a heteroscedastic mixture of two normal distributions to model the given data. To incorporate the dependency on different test settings, linear models are used for the means and the mixing proportion. Gamma distributions are proposed as priors for the model parameters, due to the physical restrictions concerning the sample space. For the variances hierarchical inverse gamma priors are applied. Sampling from the posterior is done by using Monte Carlo Markov Chain methods. The proposed mixtures-of-experts model shows good adaption to the behavior of the measurements as well as good prediction quality.

Failure probability estimation under additional subsystem information with application to semiconductor burn-in

ABSTRACT In the classical approach to qualitative reliability demonstration, system failure probabilities are estimated based on a binomial sample drawn from the running production. In this paper, we show how to take account of additional available sampling information for some or even all subsystems of a current system under test with serial reliability structure. In that connection, we present two approaches, a frequentist and a Bayesian one, for assessing an upper bound for the failure probability of serial systems under binomial subsystem data. In the frequentist approach, we introduce (i) a new way of deriving the probability distribution for the number of system failures, which might be randomly assembled from the failed subsystems and (ii) a more accurate estimator for the Clopper–Pearson upper bound using a beta mixture distribution. In the Bayesian approach, however, we infer the posterior distribution for the system failure probability on the basis of the system/subsystem testing results and a prior distribution for the subsystem failure probabilities. We propose three different prior distributions and compare their performances in the context of high reliability testing. Finally, we apply the proposed methods to reduce the efforts of semiconductor burn-in studies by considering synergies such as comparable chip layers, among different chip technologies.

An Explicit Distribution to Model the Proportion of Heating Degree Day and Cooling Degree Day

With a view to estimating the energy consumption, we derive the explicit distribution of the proportion X/(X + Y) when X and Y follow the new Bivariate Affine-Linear Exponential distribution. An application of this distribution to model the proportion of heating using the heating degree day and the cooling degree day data in the State of Alabama for Appalachian Mountain is provided. Using intensive computations based on R-program, tabulation of some quantiles associated with this particular distribution of proportion is also provided, which is quite useful in estimating the proportion of energy required to heat a building.