Multivariate Life Testing in Variably Scaled Environments

This paper examines modeling and inference questions for experiments in which different subsets of a set of k possibly dependent components are tested in r different environments. In each environment, the failure times of the set of components on test is assumed to be governed by a particular type of multivariate exponential (MVE) distribution. For any given component tested in several environments, it is assumed that its marginal failure rate varies from one environment to another via a change of scale between the environments, resulting in a joint MVE model which links in a natural way the applicable MVE distributions describing component behavior in each fixed environment. This study thus extends the work of Proschan and Sullo (1976) to multiple environments and the work of Kvam and Samaniego (1993) to dependent data. The problem of estimating model parameters via the method of maximum likelihood is examined in detail. First, necessary and sufficient conditions for the identifiability of model parameters are established. We then treat the derivation of the MLE via a numerically-augmented application of the EM algorithm. The feasibility of the estimation method is demonstrated in an example in which the likelihood ratio test of the hypothesis of equal component failure rates within any given environment is carried out.     

Estimating the Concentration of Propagules of a Plant Pathogen from Soil Dilution Plate Counts when the Response is Non-linear

The estimation of micro-organism concentrations from dilution plate data is discussed for situations where expected counts are not proportional to the amount of sample per plate. Aspects of design and analysis are investigated in relation to an alternative non-linear model in which the concentration is given by the slope at the origin. This exponential model generally provides a good fit to available experimental data. Simulations show that estimators based on the model perform well when the response is non-linear and remain reasonably efficient when the response is linear.     

Algorithms Section: Introduction

Electronic Access to Algorithms
Applied Statistics algorithms are available on Statlib at Carnegie Mellon University and on the UK mirror of Statlib at the University of Kent. They may be accessed either via anonymous file transfer protocol (FTP) or by WWW mosaic.     

Bayesian Analysis of DNA Profiling Data in Forensic Identification Applications

The utilization of DNA evidence in cases of forensic identification has become widespread over the last few years. The strength of this evidence against an individual standing trial is typically presented in court in the form of a likelihood ratio (LR) or its reciprocal (the profile match probability). The value of this LR will vary according to the nature of the genetic relationship between the accused and other possible perpetrators of the crime in the population. This paper develops ideas and methods for analysing data and evaluating LRs when the evidence is based on short tandem repeat profiles, with special emphasis placed on a Bayesian approach. These are then applied in the context of a particular quadruplex profiling system used for routine case-work by the UK Forensic Science Service.     

A Continuous Representation of the Family of Stable Law Distributions

Conventional parametric representations of stable law distributions do not allow all members of the family to be obtained as continuous limits of the parameters. Model building (or simulation) using such representations will be numerically unstable near such limits in consequence. Existing tables are not satisfactory near such limits as interpolation cannot be carried out. We show that these difficulties are overcome by using a new shifted Cartesian representation which characterizes the entire stable law family in a completely continuous way. Standardization is still possible with this representation so that tabulation, using just two bounded parameters, can be carried out. Its use is illustrated in a non-regular threshold estimation problem involving stable distributions which are discontinuous limits in conventional representations.     

Estimation of a Change Point in a Hazard Function Based on Censored Data

The hazard function plays an important role in reliability or survival studies since it describes the instantaneous risk of failure of items at a time point, given that they have not failed before. In some real life applications, abrupt changes in the hazard function are observed due to overhauls, major operations or specific maintenance activities. In such situations it is of interest to detect the location where such a change occurs and estimate the size of the change. In this paper we consider the problem of estimating a single change point in a piecewise constant hazard function when the observed variables are subject to random censoring. We suggest an estimation procedure that is based on certain structural properties and on least squares ideas. A simulation study is carried out to compare the performance of this estimator with two estimators available in the literature: an estimator based on a functional of the Nelson-Aalen estimator and a maximum likelihood estimator. The proposed least squares estimator tums out to be less biased than the other two estimators, but has a larger variance. We illustrate the estimation method on some real data sets.     

A simple estimator for a shared frailty regression model

Summary. We propose a simple estimation procedure for a proportional hazards frailty regression model for clustered survival data in which the dependence is generated by a positive stable distribution. Inferences for the frailty parameter can be obtained by using output from Cox regression analyses. The computational burden is substantially less than that of the other approaches to estimation. The large sample behaviour of the estimator is studied and simulations show that the approximations are appropriate for use with realistic sample sizes. The methods are motivated by studies of familial associations in the natural history of diseases. Their practical utility is illustrated with sib pair data from Beaver Dam, Wisconsin.     

基于DFT的MC-CDMA系统信道估计算法

基于导频辅助的最小平方(LS)算法是MC-CDMA中常用的信道估计算法,它运算量低,实现简单,但信道估计精度差。该文讨论了MC-CDMA的导频插入方式,提出一种基于离散傅里叶变换(DFT)的信道估计算法。该算法将LS信道估计循环前缀长度外的时域响应值置零,并设定阈值忽略循环长度内的噪声和无效径响应。该算法保留了LS算法运算量小和实现简单的优点,大大降低了噪声对信道估计精度的影响,仿真结果验证了算法的有效性。     

MUSIC法的有限域单向快速频率搜索

快速傅里叶变换(FFT)算法在应用中速度快,精度较低,而MUSIC法精度高,实时性差。该文通过分析两者联合谐波频率估计的可能性,提出利用FFT算法对谐波频率进行预估计,用二分法将频域划分为有限个细小的频率区间,根据可能的有效频率选取有效区间,再通过MUSIC方法在有限的搜索区间进行频率细化,并在细化的过程中采用趋向谱幅值增大的单向搜索。通过仿真研究,该方法能有效提高信号的处理速度,加强MUSIC算法的应用。     

直接序列扩频信号PN序列盲估计方法

在非协作信息侦测情况下,提出了一种实用的直接序列扩频(DS-SS)通信信号PN码序列的快速盲估计方法。该方法根据DS-SS基带信号的特点,首先采用二阶循环统计量估计PN码周期,然后利用分段互相关平均估计PN码同步始点;并在准确预估计这2个参数的基础上,将接收信号以PN码周期分成多个时段,利用多重互相关方法估计PN码序列。理论分析与仿真表明,该方法在截获信号信噪比很低的情况下有很好的估计性能,且无需任何先验知识,对PN码码型也没有任何限制,是一种有效的快速盲估计方法。