Persistence and heterogeneity in habitat selection studies using radio telemetry

Biologists attach radio transmitters to animals so that the animals' movements through their preferred habitats can be followed. To analyze the resulting sequences of visited habitat classes, McCracken, Manly, and Vander Heyden (1998, Journal of Agricultural, Biological, and Environmental Statistics 3(3), 268-279) proposed an independent multinomial selections (IMS) model. Two issues that arise when using this approach are: (i) serial dependence possibly affects measures of uncertainty; and (ii) individual animals from the population studied may exhibit heterogeneity in their selection patterns. We develop two single-parameter extensions of the IMS model to address these issues. A Markov chain model allows for persistence in the habitat class previously visited. Heterogeneity is modeled by assuming the population of animal selection patterns follows a Dirichlet distribution, from which the study animals are a random sample. We show that these persistence and heterogeneity characteristics are present in the study by McCracken et al. (1998) of bear movements. Simulations demonstrate that failure to account for persistence or heterogeneity when either is present can seriously misrepresent measures of uncertainty.     

Unified maximum likelihood estimates for closed capture-recapture models using mixtures

Agresti (1994, Biometrics 50, 494-500) and Norris and Pollock (1996a, Biometrics 52, 639-649) suggested using methods of finite mixtures to partition the animals in a closed capture-recapture experiment into two or more groups with relatively homogeneous capture probabilities. This enabled them to fit the models Mh, Mbh (Norris and Pollock), and Mth (Agresti) of Otis et al. (1978, Wildlife Monographs 62, 1-135). In this article, finite mixture partitions of animals and/or samples are used to give a unified linear-logistic framework for fitting all eight models of Otis et al. by maximum likelihood. Likelihood ratio tests are available for model comparisons. For many data sets, a simple dichotomy of animals is enough to substantially correct for heterogeneity-induced bias in the estimation of population size, although there is the option of fitting more than two groups if the data warrant it.     

遗传密码与氨基酸含量的非线性关系

本文利用回归分析方法给出了遗传密码与氨基酸含量之间的相关关系,结果表明;氨基酸的相对含量与遗传密码子数呈四次多项式回归,回归方程的复相关指数高达0.96以上,模型的平均拟合误差在2～7％之间．     

Optimum experimental designs for multinomial logistic models

Multinomial responses frequently occur in dose level experiments. For example, in a study of the influence of gamma radiation on the emergence of house flies (Musca domestica L., 1758), three disjoint outcomes occurred: death before the pupae opened, death during emergence, and life after emergence. Although the flies are easy to breed, this sort of bioassay is, in general, very expensive since it requires the use of a gamma radiation source. Experiments therefore need to be designed to involve the minimum number of different doses. Here the theory of optimum experimental design is applied to provide efficient experiments to estimate the parameters of those multinomial logistic models that are a special case of the multivariate logistic models of Glonek and McCullagh (1995, Journal of the Royal Statistical Society, Series B 57, 533-546). The purpose is to reduce the overall experimental cost. The general equivalence theorem (Fedorov, 1972, Theory of Optimal Experiments) is adapted to this class of models, providing an effective method of generating and checking the optimality of designs. One example on flies demonstrates the method, which can be easily implemented.     

一类非连续数据的GLM模型的应用

分析了因变量取有限个不连续点值的一种情况,给出了因变量Ｙ取值城中任一点值 的概率估计．并运用这种方法,对ＲＫ手术的结果作出了预测．     

Comparative <Superscript>1</Superscript>H NMR-based metabonomic analysis of HIV-1 sera

¹H NMR spectroscopy of sera from HIV-1 infected and uninfected individuals was performed on 300 and 600 MHz instruments. The resultant spectra were automatically data reduced to 90 and 180 integral segments of equal length. Analysis of variance identified significant differences between the sample groups, especially for the samples analyzed on 600 MHz and reduced to fewer segments. Linear discriminant analysis correctly classified 100% of the samples analyzed on the 300 MHz NMR (reduced to 180 segments); an increase in instrument sensitivity resulted in lower percentages of correctly classified samples. Multinomial logistic regression (MLR) resulted in 100% correct classification of all samples from both instruments. Thus ¹H-NMR metabonomics on either instrument distinguishes HIV-positive individuals using or not using anti retroviral therapy, but the sensitivity of the instrument impacts on data reduction. Furthermore, MLR is a novel multivariate statistical technique for improved classification of biological data analyzed in NMR. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A New Test for Cross-Over Clinical Trials with Order Effects

In cross-over designs for the comparison of two treatments, A and B, each patient receives both treatments in a randomized order. We will consider such design when the response of each patient is binary and propose a new test for treatment effect incorporating the information available from like pairs of response.     

Uncovering a Latent Multinomial: Analysis of Mark–Recapture Data with Misidentification

Summary .  Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture–recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture–recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies  f  are a known linear transformation    f = A ' x    of a latent multinomial variable  x  with cell probability vector    π = π ( θ )  . Given that full conditional distributions   [ θ  |  x ]   can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution   [ x  |  f , θ ]  , which is made possible by knowledge of the null space of A ' . We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks.     

Small Sample Correction for the Variance of GEE Estimators

When clustered multinomial responses are fit using the generalized logistic link, Morel (1989) introduced a small sample correction in the Taylor series based estimator of the covariance matrix of the parameter estimates. The correction reduces the bias of the Type I error rates in small samples and guarantees positive definiteness of the estimated variance‐covariance matrix. It is well known that small sample bias in the use of the Delta method persists in any application of the Generalized Estimating Equations (GEE) methodology. In this article, we extend the correction originally suggested for the generalized logistic link, to other link functions and distributions, when parameters are estimated by GEE. In a Monte Carlo study with correlated data generated under different sampling schemes, the small sample correction has been shown to be effective in reducing the Type I error rates when the number of clusters is relatively small.