基于生物免疫原理的动态入侵检测系统的研究

文章提出了一种基于生物免疫原理的动态更新入侵检测系统.并特别针对如何在此过程中得到较完备精确的self集介绍了一种动态更新方法,具体是以self集、成熟检测子、记忆检测子三者为研究对象,探讨它们各自在实际运行过程中的更新规则.     

Detecting the two complementary defectives with a balance scale

We consider the problem of finding a pair of irregular boxes from a set of n boxes using a balance scale. One irregular box is heavier and the other lighter than a regular box but the total weight of the two irregular boxes is the same as the total weight of two regular boxes. Let N(w) denote the maximum number of boxes w weighings can handle. We give a weighing scheme such that N(2t) ≥3^t for t ≥2 and N(2t + 1) ≥ 5.3^t-1 for t ≥ 1. The N(2t) result meets the information-theoretic bound and hence is the best possible.     

Detection of change points using rank methods

In this paper, the detection and estimation of change points of local parameters are studied by means of localization procedures and rank statistics. These techniques are also applied to detection and estimation of the change points of scale parameters and that of location parameters of directional data.     

Empirical null distribution-based modeling of multi-class differential gene expression detection

In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood-based approach to estimating an empirical null distribution to incorporate gene interactions and provide a more accurate false-positive control than the commonly used permutation or theoretical null distribution-based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations and application to lung transplant microarray data, we illustrate the competitive performance of the proposed method.     

Influence functions for certain parameters in multivariate analysis

The influence function introduced by Hampe1 (1968, 1973, 1974) is a tool that can be used for outlier detection. Campbell (1978) has obtained influence function for Mahalanobis’s distance between two populations which can be used for detecting outliers in discrim-inant analysis. In this paper influence functions for a variety of parametric functions in multivariate analysis are obtained. Influence functions for the generalized variance, the matrix of regression coefficients, the noncentrality matrix Σ^-1 δ in multivariate analysis of variance and its eigen values, the matrix L, which is a generalization of 1-R² , canonical correlations, principal components and parameters that correspond to Pillai’s statistic (1955), Hotelling’s (1951) generalized To² and Wilk’s Λ (1932), which can be used for outlier detection in multivariate analysis, are obtained. Delvin, Ginanadesikan and Kettenring (1975) have obtained influence function for the population correlation co-efficient in the bivariate case. It is shown in this paper that influence functions for parameters corresponding to r², R², and Mahalanobis D² can be obtained as particular cases.     

The probability distribution and the expected value of a stopping variable associated with one-sided cusum procedures for non-negative integer valued random variables

The structure of a stopping variable N based on one-sided CUSUM procedures is analyzed. Stopping occurs when a Markovian sequence of maxima of partial sums {M } crosses a certain boundary. On the basis of a recursive relationship between the M_n+1 and M_n a recursive equation is derived for the determination of the defective distributions K_n(x) = P{M ≤ x, N ≤n} . This recursive equation yields a recursive algorithm for the determination of P {N > n} . The paper studies the case when the basic random variables are non-negative integers-valued. In these cases the values of P{N > n} and E{N} can be determined by solving proper systems of linear equations.     

A scale-location adjustment for proportional hazards deviance residuals

We show that deviance residuals derived using the proportional hazards assumption (including Cox regression) are not asymptotically standard normal, but that a scale-location adjustment makes them nearly standard normal, even for moderate sample sizes. This adjustment should aid in outlier detection, as it allows a more exact assessment of when a deviance residual is unusually large.     

Change Point Detection in The Skew-Normal Model Parameters

Bayesian inference under the skew-normal family of distributions is discussed using an arbitrary proper prior for the skewness parameter. In particular, we review some results when a skew-normal prior distribution is considered. Considering this particular prior, we provide a stochastic representation of the posterior of the skewness parameter. Moreover, we obtain analytical expressions for the posterior mean and variance of the skewness parameter. The ultimate goal is to consider these results to one change point identification in the parameters of the location-scale skew-normal model. Some Latin American emerging market datasets are used to illustrate the methodology developed in this work.     

Online monitoring with local smoothing methods and adaptive ridging

We consider online monitoring of sequentially arising data as e.g. met in clinical information systems. The general focus thereby is to detect breakpoints, i.e. timepoints where the measurement series suddenly changes the general level. The method suggested is based on local estimation. In particular, local linear smoothing is combined by ridging with local constant smoothing. The procedure is demonstrated by examples and compared with other available online monitoring routines.     

A note on separated data and exact likelihood-ratio p-values in logistic regression

Exact conditional p-values based on the likelihood-ratio statistic in logistic regression require accurate computation of the supremum of the likelihood function, particularly for outcomes in the sample space that represent completely-separated or quasi-completely-separated data sets. Current software does not always handle these cases well. Three simple solutions are proposed.