Náhodové diferenciální rovnice
Neuromorphic features of probabilistic neural networks
We summarize the main results on probabilistic neural networks recently published in a series of papers. Considering the framework of statistical pattern recognition we assume approximation of class-conditional distributions by finite mixtures of product components. The probabilistic neurons correspond to mixture components and can be interpreted in neurophysiological terms. In this way we can find possible theoretical background of the functional properties of neurons. For example, the general...
New Methods for Combining Forecasts
New tendencies in industrial statistics: Is SQC in a crisis?
New variance ratio tests to identify random walk from the general mean reversion model.
Node assignment problem in Bayesian networks
This paper deals with the problem of searching for the best assignments of random variables to nodes in a Bayesian network (BN) with a given topology. Likelihood functions for the studied BNs are formulated, methods for their maximization are described and, finally, the results of a study concerning the reliability of revealing BNs' roles are reported. The results of BN node assignments can be applied to problems of the analysis of gene expression profiles.
Noninvasive monitoring of hepatic damage from hepatitis C virus infection.
Nonlinear filtering and optimal quality control.
Nonparametric bivariate estimation for successive survival times.
Several aspects of the analysis of two successive survival times are considered. All the analyses take into account the dependent censoring on the second time induced by the first. Three nonparametric methods are described, implemented and applied to the data coming from a multicentre clinical trial for HIV-infected patients. Visser's and Wang and Wells methods propose an estimator for the bivariate survival function while Gómez and Serrat's method presents a conditional approach for the second...
Nonparametric recursive aggregation process
In this work we introduce a nonparametric recursive aggregation process called Multilayer Aggregation (MLA). The name refers to the fact that at each step the results from the previous one are aggregated and thus, before the final result is derived, the initial values are subjected to several layers of aggregation. Most of the conventional aggregation operators, as for instance weighted mean, combine numerical values according to a vector of weights (parameters). Alternatively, the MLA operators...
Notes on the bias of dissimilarity indices for incomplete data sets: the case of archaelogical classification.
The problem of missing data is particularly present in archaeological research where, because of the fragmentariness of the finds, only a part of the characteristics of the whole object can be observed. The performance of various dissimilarity indices differently weighting missing values is studied on archaeological data via a simulation. An alternative solution consisting in randomly substituting missing values with character sets is also examined. Gower's dissimilarity coefficient seems to be...
Nuisance parameters in financial statistics
Numerical taxonomy: a missing link for case-based reasoning and autonomous agents.
Numerical taxonomy, which uses numerical methods to classify and relate items whose properties are non-numerical, is suggested as both an advantageous tool to support case-based reasoning and a means for agents to exploit knowledge that is best expressed in cases. The basic features of numerical taxonomy are explained, and discussed in application to a problem where human agents with differing views obtain solutions by negotiation and by reference to knowledge that is essentially case-like: allocation...