Distributions of statistics describing concentration of runs in non homogeneous Markov-dependent trials

In the first n, n ? 3, trials of a non homogeneous zero-one Markov chain of first order, we consider runs of ones of length exceeding a threshold. The article deals with statistics denoting, the length and the position of the shortest segment of the chain in which all such runs of ones are concentrated. The study provides recursive schemes for conditional distributions of these statistics. Numerical examples illustrate the theoretical results.     

Robust modified profile estimating function with application to the generalized estimating equation

We consider methods for reducing the effect of fitting nuisance parameters on a general estimating function, when the estimating function depends on not only a vector of parameters of interest, θ$\theta$, but also on a vector of nuisance parameters, λ$\lambda$. We propose a class of modified profile estimating functions with plug-in bias reduced by two orders. A robust version of the adjustment term does not require any information about the probability mechanism beyond that required by the original estimating function. An important application of this method is bias correction for the generalized estimating equation in analyzing stratified longitudinal data, where the stratum-specific intercepts are considered as fixed nuisance parameters, the dependence of the expected outcome on the covariates is of interest, and the intracluster correlation structure is unknown. Furthermore, when the quasi-scores for θ$\theta$ and λ$\lambda$ are available, we propose an additional multiplicative adjustment term such that the modified profile estimating function is approximately information unbiased. This multiplicative adjustment term can serve as an optimal weight in the analysis of stratified studies. A brief simulation study shows that the proposed method considerably reduces the impact of the nuisance parameters.     

广义数学期望

本文应用 H.L.P.均值于样本,构造了广义样本均值;为了将它用于参数估计,根据替换法则(Substitution rule),推广了数学期望的定义,同时引入新的无偏性.最后应用广义数学期望的基本性质推出若干结论(定理1～4),说明在参数估计方面应用广义均值是有意义的.     

A method for consideration of conditional dependencies in the Fellegi and Sunter model of record linkage

An objective of Record Linkage is to link two data files by identifying common elements. A popular model for doing the separation is the probabilistic one from Fellegi and Sunter. To estimate the parameters needed for the model usually a mixture model is constructed and the EM algorithm is applied. For simplification, the assumption of conditional independence is often made. This assumption says that if several attributes of elements in the data are compared, then the results of the comparisons regarding the several attributes are independent within the mixture classes. A mixture model constructed with this assumption has been often used. Within this article a straightforward extension of the model is introduced which allows for conditional dependencies but is heavily dependent on the choice of the starting value. Therefore also an estimation procedure for the EM algorithm starting value is proposed. The two models are compared empirically in a simulation study based on telephone book entries. Particularly the effect of different starting values and conditional dependencies on the matching results is investigated.     

一类平衡阶数为负整数的非线性偏微分方程的精确解

以齐次平衡原理为基础,给出了平衡阶数为负整数时的求解非线性偏微分方程的基本方法,并对方程ut=αuuxx+βu2x+p(u-u2)进行求解,到得了它的两个不同形式的精确解。     

Confidence Intervals Following Group Sequential Tests in Clinical Trails with Multivariate Observations

Several authors ( e.g. Kim and DeMets, 1987a, Biometrics) have developed methods for estimation following group sequential tests in clinical trials when each patient has only one response. In many long-term clinical trials, the subjects enter the study sequentially and yield repeated measurements or other types of multivariate observations at successive follow-up visits. Typically, investigators want to compare a parameter of interest such as the slope over time in a repeated measures trial etc. In this article, we propose an exact confidence interval for these parameters in a repeated measures trial, and compare it with a naive confidence interval using Monte Carlo simulation. This method is illustrated with a real example for bone density measurements.     

Pseudo-perfect and adaptive variants of the Metropolis–Hastings algorithm with an independent candidate density

We describe and examine an imperfect variant of a perfect sampling algorithm based on the Metropolis–Hastings algorithm that appears to perform better than a more traditional approach in terms of speed and accuracy. We then describe and examine an ‘adaptive’ Metropolis–Hastings algorithm which generates and updates a self-target candidate density in such a way that there is no ‘wrong choice’ for an initial candidate density. Simulation examples are provided.     

Exact tests for two-way symmetriccontingency tables

A two-way contingency table in which both variables have the same categories is termed a symmetric table. In many applications, because of the social processes involved, most of the observations lie on the main diagonal and the off-diagonal counts are small. For these tables, the model of independence is implausible and interest is then focussed on the off-diagonal cells and the models of quasi-independence and quasi-symmetry. For ordinal variables, a linear-by-linear association model can be used to model the interaction structure. For sparse tables, large-sample goodness-of-fit tests are often unreliable and one should use an exact test. In this paper, we review exact tests and the computing problems involved. We propose new recursive algorithms for exact goodness-of-fit tests of quasi-independence, quasi-symmetry, linear-by-linear association and some related models. We propose that all computations be carried out using symbolic computation and rational arithmetic in order to calculate the exact p-values accurately and describe how we implemented our proposals. Two examples are presented.     

Perfect simulation of conditionally specified models

We discuss how the ideas of producing perfect simulations based on coupling from the past for finite state space models naturally extend to multivariate distributions with infinite or uncountable state spaces such as auto-gamma, auto-Poisson and autonegative binomial models, using Gibbs sampling in combination with sandwiching methods originally introduced for perfect simulation of point processes.     

On the symmetry of m-estimators computed by the huber-dutter algorithm

An M-estimator of the Linear regression parameter computed by the Huber and Dutter (1974) algorithm is summetrically distributed about the true value, under reasonably mild conditions.