INCOMPLETE DATA IN GENERALIZED LINEAR MODELS WITH CONTINUOUS COVARIATES

This paper proposes a method for estimating the parameters in a generalized linear model with missing covariates. The missing covariates are assumed to come from a continuous distribution, and are assumed to be missing at random. In particular, Gaussian quadrature methods are used on the E-step of the EM algorithm, leading to an approximate EM algorithm. The parameters are then estimated using the weighted EM procedure given in Ibrahim (1990). This approximate EM procedure leads to approximate maximum likelihood estimates, whose standard errors and asymptotic properties are given. The proposed procedure is illustrated on a data set.     

Conditioning of Marked Point Processes within a Bayesian Framework

Shale units with low permeability create barriers to fluid flow in a sandstone reservoir. A spatial stochastic model for the location of shale units in a reservoir is defined. The model is based on a marked point process formulation, where the marks are parameterized by random functions for the shape of a shale unit. This extends the traditional formulation in the sense that conditioning on the actual observations of the shale units is allowed in an arbitrary number of wells penetrating the reservoir. The marked point process for the shale units includes spatial interaction of units and allows a random number of units to be present. The model is defined in a Bayesian setting with prior pdfs assigned to size–shape parameters of shale units. The observations of shales in wells are associated with a likelihood function. The posterior pdf of the marked point process can only partially be developed analytically; the final solution must be determined by sampling using the Metropolis–Hastings algorithm. An example is presented, demonstrating the consequences of increasing the number of wells in which observations are made.     

The Construction of Efficient Two-replicate Row–Column Designs for Use in Field Trials

Two-replicate row–column designs are often used for field trials in multisite tree or plant breeding programmes. With only two replicates for each trial, it is important to use designs with optimal or near optimal efficiency factors. This paper presents an algorithm for generating such designs. The method extends the contraction approach of Bailey and Patterson to any set of parameters and uses the factorial design construction algorithm of Williams and John to generate designs. Our experience with the algorithm is that it produces designs that are at least as good as, and often much better and more quickly generated than, those obtained by other recent computer algorithms.     

运输路径问题的一个新启发式算法

在解运输路径问题时常常使用Clarke和Wright提出的启发式算法(下称C.W.算法)。本文对Clarke和Wright算法进行改进。改进算法的计算复杂性虽不如C.W.算法,但计算的数值结果在大多数情况下比C.W.算法的结果更好。     

A Problem of Dimensionality in Normal Mixture Analysis

Suppose the p -variate random vector W , partitioned into q variables W₁ and p - q variables W₂, follows a multivariate normal mixture distribution. If the investigator is mainly interested in estimation of the parameters of the distribution of W₁, there are two possibilities: (1) use only the data on W₁ for estimation, and (2) estimate the parameters of the p -variate mixture distribution, and then extract the estimates of the marginal distribution of W₁. In this article we study the choice between these two possibilities mainly for the case of two mixture components with identical covariance matrices. We find the asymptotic distribution of the linear discriminant function coefficients using the work of Efron (1975 ) and O'Neill (1978 ), and give a Wald–test for redundancy of W₂. A simulation study gives further insights into conditions under which W₂ should be used in the analysis: in summary, the inclusion of W₂ seems justified if Δ 2.1, the Mahalanobis distance between the two component distributions based on the conditional distribution of W₂ given W₁, is at least 2.     

Estimation of General Stationary Processes by Variable Length Markov Chains

We develop new results about a sieve methodology for the estimation of minimal state spaces and probability laws in the class of stationary processes defined on finite categorical spaces. Using a sieve approximation with variable length Markov chains of increasing order, we show that an adapted version of the Context algorithm yields asymptotically correct estimates for the minimal state space and for the underlying probability distribution. As a side product, the method of sieves yields a nice graphical tree representation for the potentially infinite dimensional minimal state space of the data generating process, which is very useful for exploration of the memory.     

Combining information from multiple surveys through the empirical likelihood method

It is often desirable to combine information collected in compatible multiple surveys in order to improve estimation and meet consistency requirements. Zieschang (1990) and Renssen & Nieuwenbroek (1997) suggested to this end the use of the generalized regression estimator with enlarged number of auxiliary variables. Unfortunately, adjusted weights associated with their approach can be negative. The author uses the notion of pseudo empirical likelihood to construct new estimators that are consistent, efficient and possess other attractive properties. The proposed approach is asymptotically equivalent to the earlier one, but it has clear maximum likelihood interpretations and its adjusted weights are always positive. The author also provides efficient algorithms for computing his estimators.     

混杂纤维混凝土抗弯冲击性能研究

研究了掺加异型塑钢纤维、铜纤维以及这两种纤维混杂的混凝土梁的抗弯冲击性能。测定了在不同纤维掺量下混凝土梁的初裂冲击次数、破坏冲击次数以及冲击能。试验结果表明：混掺纤维比单掺纤维显著提高了混凝土的冲击能和延性，但对初裂性能影响不大。     

用隐马尔可夫模型设计人脸表情识别系统

根据隐马尔可夫模型(HMM)的基本理论和算法设计了一个人脸表情识别系统。该系统由两层HMM组成:低层由六个HMM组成,分别对应六种特定表情。人脸表情特征向量进入系统后,经过低层HMM初步识别,其结果组成高层HMM的观察向量,经过高层HMM解码,确认出表情,从而提高了系统的识别率,增强了系统的健壮性。     

Markov chain Monte Carlo exact inference for binomial and multinomial logistic regression models

We develop Metropolis-Hastings algorithms for exact conditional inference, including goodness-of-fit tests, confidence intervals and residual analysis, for binomial and multinomial logistic regression models. We present examples where the exact results, obtained by enumeration, are available for comparison. We also present examples where Monte Carlo methods provide the only feasible approach for exact inference.