Computation of probability and non-centrality parameter of a non-central f-distribution

The small sample properties of the score function approximation to the maximum likelihood estimator for the three-parameter lognormal distribution using an alternative parameterization are considered. The new set of parameters is a continuous function of the usual parameters. However, unlike with the usual parameterization, the score function technique for this parameterization is extremely insensitive to starting values. Further, it is shown that whenever the sample third moment is less than zero, a local maximum to the likelihood function exists at a boundary point. For the usual parameterization, this point is unattainable. However, the alternative parameter space can be expanded to include these boundary points. This procedure results in good estimates of the expected value, variance, extreme percentiles and other parameters of the distribution even in samples where, with the typical parameterization, the estimation procedure fails to converge.     

Convex design theory 1

Bayesian fixed sample size estimation and sequential estimation of the parameters of the two parameter uniform distribution are discussed.     

Effects of fragmentation and trampling on carabid beetle assemblages in urban woodlands in Helsinki, Finland

We studied the effects of fragmentation (edge effects and patch size) and trampling (path cover) on carabid beetle assemblages in urban woodland patches in Helsinki, Finland. We expected that (1) open habitat and generalist species would benefit and forest species would suffer from increased woodland fragmentation, and (2) most carabid species would respond negatively to increased levels of trampling. A total of 2088 carabid individuals representing 37 species were collected. A cluster analysis distinguished sites in the interior of large woodland patches, with low or moderate path cover, from the other sites. The other sites did not cluster meaningfully, suggesting increased variation in the carabid fauna with increasing human impact. All species and ecological species-groups decreased with increasing distance from the edge toward the woodland interior and with increasing patch size. This pattern is in accordance with our expectation for open habitat and generalist species but opposite to what we expected for forest species. The reason for these surprising results may be that (1) the species we collected are not true forest interior species, (2) urban woodland edges are optimal habitats for many forest carabids, or (3) edges are actually sub-optimal, and high catches simply reflect increased activity of beetles moving away from the edge. Trampling did not have an overall negative effect on carabids as hypothesized. Species associated with moist forest habitat responded as predicted: they decreased in abundance with increasing path cover. Furthermore, open habitat species decreased with increasing path cover but more straightforward than we had predicted. Model elaboration, by dropping the highly trampled sites from the analyses, suggested that our data of high trampling may be too scarce: the results without these sites were more in accordance with our predictions than with the full dataset.     

WINDOWS环境下数据采集控制软件的研究

提出了在ＷＩＮＤＯＷＳ环境下利用汇编语言直接对硬件端口进行实时访问的一种方法,利用该方法创建的动态连接库（ＤＬＬ）可以被多种高级语言调用,有效地解决了在Ｖｉｓｕａｌ Ｂａｓｉｃ等开发环境下不能控制硬件的问题。     

On triple sampling scremes for estimating from binomial data with misclassification errors ∗

Previous work has been carried out on the use of double sampling schemes for inference from binomial data which are subject to misclassification. The double sampling scheme utilizes a sample of n units which are classified by both a fallible and a true device and another sample of n₂ units which are classified only by a fallible device. A triple sampljng scheme incorporates an additional sample of n_l units which are classified only by the true device. In this paper we apply this triple sampling to estimation from binomialdata. First estimation of a binomial proportion is discussed under different misclassification structures. Then, the problem of optimal allocation of sample sizes is discussed.     

Some aspects of the design and analysis of cluster randomization trials

Trials which randomize intact social groups, or clusters, to different interventions are becoming increasingly widespread. Although statistically less efficient than trials which randomize individuals, such designs are often preferred from a practical or ethical point of view, particularly in the evaluation of health care or educational strategies. We discuss selected issues that arise in the conduct of such trials, including the choice of design, ethical implications, sample size estimation and approaches to the analysis. The discussion is closely tied to methodological issues that have arisen in a recent evaluation trial of a new antenatal care programme, as sponsored by the Special Programme of Research, Development and Research Training in Human Reproduction of the World Health Organization.     

On Conditional and Partial Correlation

A graphical technique, similar in spirit to probability plotting, can be used to judge whether a Poisson model is appropriate for an observed frequency distribution. This “Poissonness plot” can equally be applied to truncated Poisson situations. It provides a type of robustness for detecting isolated discrepancies in otherwise well-behaved frequency distributions.     

Sample-based Maximum Likelihood Estimation of the Autologistic Model

New recursive algorithms for fast computation of the normalizing constant for the autologistic model on the lattice make feasible a sample-based maximum likelihood estimation (MLE) of the autologistic parameters. We demonstrate by sampling from 12 simulated 420×420 binary lattices with square lattice plots of size 4×4, …, 7×7 and sample sizes between 20 and 600. Sample-based results are compared with ‘benchmark’ MCMC estimates derived from all binary observations on a lattice. Sample-based estimates are, on average, biased systematically by 3%–7%, a bias that can be reduced by more than half by a set of calibrating equations. MLE estimates of sampling variances are large and usually conservative. The variance of the parameter of spatial association is about 2–10 times higher than the variance of the parameter of abundance. Sample distributions of estimates were mostly non-normal. We conclude that sample-based MLE estimation of the autologistic parameters with an appropriate sample size and post-estimation calibration will furnish fully acceptable estimates. Equations for predicting the expected sampling variance are given.     

Testing and Merging Information for Effect Size Estimation

A large-sample test for testing the equality of two effect sizes is presented. The null and non-null distributions of the proposed test statistic are derived. Further, the problem of estimating the effect size is considered when it is a priori suspected that two effect sizes may be close to each other. The combined data from all the samples leads to more efficient estimator of the effect size. We propose a basis for optimally combining estimation problems when there is uncertainty concerning the appropriate statistical model-estimator to use in representing the sampling process. The objective here is to produce natural adaptive estimators with some good statistical properties. In the context of two bivariate statistical models, the expressions for the asymptotic mean squared error of the proposed estimators are derived and compared with the parallel expressions for the benchmark estimators. We demonstrate that the suggested preliminary test estimator has superior asymptotic mean squared error performance relative to the benchmark and pooled estimators. A simulation study and application of the methodology to real data are presented.     

Comparison of non-parametric and semi-parametric tests in detecting long memory

The first two stages in modelling times series are hypothesis testing and estimation. For long memory time series, the second stage was studied in the paper published in [M. Boutahar et al., Estimation methods of the long memory parameter: monte Carlo analysis and application, J. Appl. Statist. 34(3), pp. 261–301.] in which we have presented some estimation methods of the long memory parameter. The present paper is intended for the first stage, and hence completes the former, by exploring some tests for detecting long memory in time series. We consider two kinds of tests: the non-parametric class and the semi-parametric one. We precise the limiting distribution of the non-parametric tests under the null of short memory and we show that they are consistent against the alternative of long memory. We perform also some Monte Carlo simulations to analyse the size distortion and the power of all proposed tests. We conclude that for large sample size, the two classes are equivalent but for small sample size the non-parametric class is better than the semi-parametric one.