Quasi Likelihood/Moment Method for Generalized and Restricted Generalized Poisson Regression Models and Its Application

This paper reviews the generalized Poisson regression model, the restricted generalized Poisson regression model and the mixed Poisson regression (negative binomial regression and Poisson inverse Gaussian regression) models which can be used for regression analysis of counts. The aim of this study is to demonstrate the quasi likelihood/moment method, which is used for estimation of the parameters of mixed Poisson regression models, also applicable to obtain the estimates of the parameters of the generalized Poisson regression and the restricted generalized Poisson regression models. Besides, at the end of this study an application related to this method for zoological data is given.     

Analysis of a Generalized Linear Mixed Model: A Case Study and Simulation Results

A class of generalized linear mixed models can be obtained by introducing random effects in the linear predictor of a generalized linear model, e.g. a split plot model for binary data or count data. Maximum likelihood estimation, for normally distributed random effects, involves high-dimensional numerical integration, with severe limitations on the number and structure of the additional random effects. An alternative estimation procedure based on an extension of the iterative re-weighted least squares procedure for generalized linear models will be illustrated on a practical data set involving carcass classification of cattle. The data is analysed as overdispersed binomial proportions with fixed and random effects and associated components of variance on the logit scale. Estimates are obtained with standard software for normal data mixed models. Numerical restrictions pertain to the size of matrices to be inverted. This can be dealt with by absorption techniques familiar from e.g. mixed models in animal breeding. The final model fitted to the classification data includes four components of variance and a multiplicative overdispersion factor. Basically the estimation procedure is a combination of iterated least squares procedures and no full distributional assumptions are needed. A simulation study based on the classification data is presented. This includes a study of procedures for constructing confidence intervals and significance tests for fixed effects and components of variance. The simulation results increase confidence in the usefulness of the estimation procedure.     

Zero-Altered and other Regression Models for Count Data with Added Zeros

On occasion, generalized linear models for counts based on Poisson or overdispersed count distributions may encounter lack of fit due to disproportionately large frequencies of zeros. Three alternative types of regression models that utilize all the information and explicitly account for excess zeros are examined and given general formulations. A simple mechanism for added zeros is assumed that directly motivates one type of model, here called the added-zero type, particular forms of which have been proposed independently by D. LAMBERT (1992) and in unpublished work by the author. An original regression formulation (the zero-altered model) is presented as a reduced form of the two-part model for count data, which is also discussed. It is suggested that two-part models be used to aid in development of an added-zero model when the latter is thought to be appropriate.     

Spatially correlated disturbances in a locally dispersing population model

The basic contact process in continuous time is studied, where instead of single occupied sites becoming empty independently, larger-scale disturbance events simultaneously remove the population from contiguous blocks of sites. Stochastic spatial simulations and pair approximations were used to investigate the model. Increasing the spatial scale of disturbance events increases spatial clustering of the population and variability in growth rates within localized regions, reduces the effective overall population density, and increases the critical reproductive rate necessary for the population to persist. Pair approximations yield a closed-form analytic expression for equilibrium population density and the critical value necessary for persistence.     

A semiparametric model for the analysis of recurrent-event panel data

In many longitudinal studies, interest focuses on the occurrence rate of some phenomenon for the subjects in the study. When the phenomenon is nonterminating and possibly recurring, the result is a recurrent-event data set. Examples include epileptic seizures and recurrent cancers. When the recurring event is detectable only by an expensive or invasive examination, only the number of events occurring between follow-up times may be available. This article presents a semiparametric model for such data, based on a multiplicative intensity model paired with a fully flexible nonparametric baseline intensity function. A random subject-specific effect is included in the intensity model to account for the overdispersion frequently displayed in count data. Estimators are determined from quasi-likelihood estimating functions. Because only first- and second-moment assumptions are required for quasi-likelihood, the method is more robust than those based on the specification of a full parametric likelihood. Consistency of the estimators depends only on the assumption of the proportional intensity model. The semiparametric estimators are shown to be highly efficient compared with the usual parametric estimators. As with semiparametric methods in survival analysis, the method provides useful diagnostics for specific parametric models, including a quasi-score statistic for testing specific baseline intensity functions. The techniques are used to analyze cancer recurrences and a pheromone-based mating disruption experiment in moths. A simulation study confirms that, for many practical situations, the estimators possess appropriate small-sample characteristics.     

On the equivalence of case-crossover and time series methods in environmental epidemiology

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.     

Dermatoglyphic variation in Europe

We describe the geographic variation patterns of 236 dermatoglyphic variables (118 for each sex) for 74 samples in Europe. Using principal components analysis and rotating to simple structure, we simplified these patterns to the first 20 axes, representing 74.2% of covariation. We then used heterogeneity tests, interpolated surfaces, one-dimensional and directional correlograms, and distances between correlograms to analyze the factor scores of these 20 axes. We also ordinated the 74 localities. The data are remarkable for showing little spatial autocorrelation, despite significant heterogeneity among localities. Only three factor axes exhibit consistently significant correlograms, indicating that there are few spatial patterns in the original variables in Europe. Almost all correlations between pairs of variables occur within serially homologous character sets and are thus developmentally determined. There is some support for demic diffusion from the southeast in finger patterns and ridge counts. We compare these results to those of previous studies and note that Lapps and Icelanders are outliers with respect to both genetics and finger tip variables, whereas Tatars are outliers with respect to craniometrics and dermatoglyphics. © 1996 Wiley-Liss, Inc.     

具有空间自相关残差的回归模型及其应用

提出了具有空间自相关残差的多变量回归问题,这种残差包括单点、局部和区域化三种尺度.文中提出了两种处理空间自相关的方法:把自相关加入残差协方差作参数的改进估计──相邻相关方法和把残差作区域化最优自相关估计──一般空间自相关方法.用森林对舞毒蛾危害敏感性概率的大范围估计为例,比较了传统回归方法和本文方法间的差异,交叉检验结果表明本文的方法明显优于直接回归方法,这说明加入空间自相关对空间回归问题是必要的.     

Geographic covariation of hosts und parasites: Evidence from Populus und Pemphigus1

The geographic covariation of the eastern cottonwood Populus deltoides with three different gallforming aphids in the genus Pemphigus is studied over eastern North America. Ten vegetative Populus characters were analyzed together with 32 stem mother, slate und gall dimension characters in Pemphigus populicaulis und in two morphs of P. populitransversus. The number of locality samples studied ranges from 56 to 157. The covariation between host und parasite characters was examined by correlation analysis und canonical correlations und shown to be slight. Multiple regressions of factor scores of Pemphigus variables on Populus characters show relatively low percentages of explained variance und few significant partial regression coefficients. Spatial autocorrelation analyses of Pemphigus factor scores und of their residuals on Populus variables demonstrated largely independent spatial structure of the two sets of variables. These findings were confirmed by multiple Mantel tests of distance matrices based on cottonwood und aphid phenetics und on geography. The undoubtedly close Pemphigus-Populus coevolution over geological time is not reflected in the microevolutionary variation over geographic space. Possible esplanations for these surprising findings are discussed.