Significance thresholds of onesided tests for means of bivariate normally distributed variables

Tests at hypotheses on the expectation vaiues ot normally distributed bivdriaU random variables, which arc based on single component tests, require the determination of the relation between the single significance thresholds and the overall error of the first kind probability. It is shown that a simple approximation can be applied to negatively correlated variables even if only the ratio of the variances is known. Numerical calculations are presented for the general case. Two examples are given.     

Detection of intrusions in information systems by sequential change-point methods

Sequential multi-chart detection procedures for detecting changes in multichannel sensor systems are developed. In the case of complete information on pre-change and post-change distributions, the detection algorithm represents a likelihood ratio-based multichannel generalization of Page’s cumulative sum (CUSUM) test that is applied to general stochastic models that may include correlated and nonstationary observations. There are many potential application areas where it is necessary to consider multichannel generalizations and general statistical models. In this paper our main motivation for doing so is network security: rapid anomaly detection for an early detection of attacks in computer networks that lead to changes in network traffic. Moreover, this kind of application encourages the development of a nonparametric multichannel detection test that does not use exact pre-change (legitimate) and post-change (attack) traffic models. The proposed nonparametric method can be effectively applied to detect a wide variety of attacks such as denial-of-service attacks, worm-based attacks, port-scanning, and man-in-the-middle attacks. In addition, we propose a multichannel CUSUM procedure that is based on binary quantized data; this procedure turns out to be more efficient than the previous two algorithms in certain scenarios. All proposed detection algorithms are based on the change-point detection theory. They utilize the thresholding of test statistics to achieve a fixed rate of false alarms, while allowing changes in statistical models to be detected “as soon as possible”. Theoretical frameworks for the performance analysis of detection procedures, as well as results of Monte Carlo simulations for a Poisson example and results of detecting real flooding attacks, are presented.     

Principles of children's gynecology in Czechoslovakia

This paper documents specific aspects of sex and eroticism as well as related aspects of social development in a small group of postpubertal male patients diagnosed as having hypopituitarism secondarily to the surgical removal of a pituitary tumor. The data include information about erection, ejaculation, masturbation, erotic imagery and intercourse, as well as dating, socializing and falling in love. Problems of case management are also discussed.     

The DYNAMO-HIA Model: An Efficient Implementation of a Risk Factor/Chronic Disease Markov Model for Use in Health Impact Assessment (HIA)

In Health Impact Assessment (HIA), or priority-setting for health policy, effects of risk factors (exposures) on health need to be modeled, such as with a Markov model, in which exposure influences mortality and disease incidence rates. Because many risk factors are related to a variety of chronic diseases, these Markov models potentially contain a large number of states (risk factor and disease combinations), providing a challenge both technically (keeping down execution time and memory use) and practically (estimating the model parameters and retaining transparency). To meet this challenge, we propose an approach that combines micro-simulation of the exposure information with macro-simulation of the diseases and survival. This approach allows users to simulate exposure in detail while avoiding the need for large simulated populations because of the relative rareness of chronic disease events. Further efficiency is gained by splitting the disease state space into smaller spaces, each of which contains a cluster of diseases that is independent of the other clusters. The challenge of feasible input data requirements is met by including parameter calculation routines, which use marginal population data to estimate the transitions between states. As an illustration, we present the recently developed model DYNAMO-HIA (DYNAMIC MODEL for Health Impact Assessment) that implements this approach.     

Neurodegeneration in normal brain aging and disease

Normal "healthy" aging is defined as aging without disease. Many aged people do not exhibit symptoms of disease and lead normal lives, but nonetheless display pathological changes that are characteristic of Alzheimer's disease (AD), Parkinson's disease (PD), dementia with Lewy bodies (DLB), and/or cerebrovascular disease (CVD). These changes are restricted to distinct brain regions and might represent preclinical stages of these disorders. This Perspective discusses arguments in favor of and against the hypothesis that pathological changes related to AD, PD, DLB, and CVD in the brains of nondemented elderly people represent early stages of these diseases rather than healthy aging. We conclude that early pathological disease-related changes do indeed constitute the beginning of AD, PD, DLB, and CVD rather than normal concomitants of aging, even in the absence of any clinical symptoms. Aging is, therefore, a major risk factor for these diseases but does not necessarily lead to age-related diseases.