WDM网双故障的共享子通路保护算法

研究了WDM网中双故障导致的多链路失效问题,提出了一种共享子通路保护算法。共享子通路保护为每条业务请求计算一条最短的工作路由,并为工作路由上每段子通路各计算两条共享风险链路组分离且代价最小的保护路由。仿真表明,共享子通路保护不仅能有效地保护双故障导致的多链路失效,而且能在资源利用率(阻塞率)和保护切换时间之间进行性能折衷。     

Basic notions for the analysis of large two-mode networks

Many large real-world networks actually have a two-mode nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many ad hoc tools have been designed for the study of special cases, very few exist to analyse (describe, extract relevant information) such networks in a systematic way. We propose here an extension of the most basic notions used nowadays to analyse large one-mode networks (the classical case) to the two-mode case. To achieve this, we introduce a set of simple statistics, which we discuss by comparing their values on a representative set of real-world networks and on their random versions. This makes it possible to evaluate their relevance in capturing properties of interest in two-mode networks.     

Estimation for hidden Markov random fields

A finite state Markov random field is noisily observed via a second finite state process. The parameters of the model are estimated, as well as the most likely signal given the observations.     

Using sibling data to understand the impact of family group decision-making on child welfare outcomes

Few studies have examined the impact of family group decision-making (FGDM) on child welfare outcomes. Studies in this area have often suffered from small sample sizes and lack of adequate comparison groups. Since FGDM is administered at the family level, sibling data provides an ideal way to compensate for small sample sizes and low enrollment rates in research evaluating FGDM. This study utilized sibling data from California's Title IV-E Waiver Demonstration Project Evaluation in Fresno and Riverside Counties to compare child welfare outcomes for children of families randomly assigned to receive FGDM (Fresno County, n = 110; Riverside County, n = 87) to children of families assigned to receive traditional child welfare services (Fresno County, n = 74; Riverside County, n = 52). After controlling for clustering effects using general estimating equations (GEE), group differences in child maltreatment, placement stability, and permanence were modeled using linear and logistic regression. Outcomes from both counties suggested no group differences. Though neutral outcomes on child welfare indicators may bring into question FGDM's efficacy for changing child welfare outcomes, this study confirms previous findings with smaller sample sizes on the impact of FGDM and demonstrates a methodological improvement over individual or family-case analyses.     

Likelihood Estimation for Censored Random Vectors

This article shows how to construct a likelihood for a general class of censoring problems. This likelihood is proven to be valid, i.e. its maximizer is consistent and the respective root-n estimator is asymptotically efficient and normally distributed under regularity conditions. The method generalizes ordinary maximum likelihood estimation as well as several standard estimators for censoring problems (e.g. tobit type I-tobit type V).     

On random variate generation for the generalized hyperbolic secant distributions

We give random variate generators for the generalized hyperbolic secant distribution and related families such as Morris's skewed generalized hyperbolic secant family and a family introduced by Laha and Lukacs. The rejection method generators are uniformly fast over the parameter space and are based upon a complex function representation of the distributions due to Harkness and HarknessThe author's research was sponsored by NSERC Grant A3456 and FCAR Grant 90-ER-0291.     

The random robin problem for laplace's equation

A random generalized solution to the Robin problem for Laplace's equation is defined in terms of the sections of the random boundary data. Existence, uniqueness, and properties are established for such a solution. Particularizations to the Dirichlet and Newmann problems as well as generalization to the Robin-Poisson problem are mentioned. Applications of the results are provided.     

On regressing regression coefficients

This paper considers a regression model in which coefficients obtained from a previous regression are themselves the object of analysis. It is shown that the parameters of interest can be obtained in two ways: pooling across observations and subsamples, or a two-stage process of first estimating the coefficients within each subsample, and then using these coefficients as dependent variables in a second stage regression. The relative properties of these estimators are analyzed, and the conditions under which the two estimators are equivalent are derived.     

A consistent system of conditional probabilities which is not compatible with any random field

A consistent, Markovian family of conditional densities is constructed which is not compatible with any random field.