A Rapid and Efficient Estimation Procedure for the Negative Binomial Distribution

This paper presents a new noniterative procedure for estimating the parameters of a negative binomial distribution. The procedure uses the first moment equation and an equation based on the weighted sample mean, with weights ω_x∝ α^z. The selection of a value for α is examined. A simulation study has been carried out and also the method has been applied to the 35 data sets analysed by Martin and Katti (1965, Biometrics) in order to compare it with the method of moments and with the method of maximum likelihood (ML). We conclude that the new procedure has greater relative efficiency than the method of moments; it gives estimates which are consistently close to ML and are easy to calculate.     

Improved Identification of Parameters in the Wolgograd Growth Model for Sugar Beets

Starting point of the investigations is the time-invariant Wolgograd model applied to a sample of sugar beets. To overcome the weak multicollinearity of the model in its logarithmic form, a ridge-type estimator is applied which uses prior information on the unknown regression coefficients. This is done by introducing the biased minimax-linear estimator. To judge the goodness of the estimates there are calculated the minimax risks of the MILE and the OLSE as well as the estimated maximal crop yields.     

西安市学生中从膝部长骨干骺融合推算身高与年龄的回归方程

本文根据西安市2612名学生的活体测量和1361例的X线拍片观察,得出了应用膝部长骨的干骺融合程度及某些线性度量推算身高与年龄的三十个回归方程和相应的误差,同时,对得出的方程进行了讨论。     

Addendum to counting by weighing and the seed testing problem

The subject of counting by weighing as addressed in Ridout & Suntheralingam (1997) is revisited. When the coefficient of variation of the seed weights is known, we show how to modify the seed testing procedure in the aforementioned paper to account for the uncertainty introduced by obtaining a given number of seeds by weight, rather than exact counting. We find that counting by weighing requires a greater number of seeds per sub‐sample than that required by the exact counting method of Ridout & Roberts (1997). Furthermore, the number of seeds per sub‐sample increases with decreasing initial sample size used to estimate mean seed weight.     

An elusive paleodemography? A comparison of two methods for estimating the adult age distribution of deaths at late Classic Copan,Honduras

Comparison of different adult age estimation methods on the same skeletal sample with unknown ages could forward paleodemographic inference, while researchers sort out various controversies. The original aging method for the auricular surface (Lovejoy et al., 1985a) assigned an age estimation based on several separate characteristics. Researchers have found this original method hard to apply. It is usually forgotten that before assigning an age, there was a seriation, an ordering of all available individuals from youngest to oldest. Thus, age estimation reflected the place of an individual within its sample. A recent article (Buckberry and Chamberlain, 2002) proposed a revised method that scores theses various characteristics into age stages, which can then be used with a Bayesian method to estimate an adult age distribution for the sample. Both methods were applied to the adult auricular surfaces of a Pre-Columbian Maya skeletal population from Copan, Honduras and resulted in age distributions with significant numbers of older adults. However, contrary to the usual paleodemographic distribution, one Bayesian estimation based on uniform prior probabilities yielded a population with 57% of the ages at death over 65, while another based on a high mortality life table still had 12% of the individuals aged over 75 years. The seriation method yielded an age distribution more similar to that known from preindustrial historical situations, without excessive longevity of adults. Paleodemography must still wrestle with its elusive goal of accurate adult age estimation from skeletons, a necessary base for demographic study of past populations.     

Adaptive Methods of Trend Detection and Their Application in Analysing Biosignals

In the study estimations of the linear trend and its slope of discrete deterministic and stochastic signals are introduced, which are based on adaptive methods of mean estimation. Because of its simple recursive nature both the trend and the slope estimation may be realized in real time and thus they may be used for on-line procedures e.g. in monitoring systems of intensive care units. The adaptive estimation functions are self-learning, that means they are able to adapt automatically to the new conditions after structure changes of the time series and thus may be also used for detecting points of structure fracture. The properties of the adaptive trend- and slope estimations are demonstrated by some characteristic simulation examples and the possibilities of their applications for analysing biological signals are investigated in three different medical cases.     

A Procedure for the Common Estimation of Parameters Corresponding to Several Treatment Groups

In this article we give a procedure for the common estimation of parameters corresponding to several treatment groups. Thereby we assume that the distribution functions of the groups belong to the same family and differ only in the parameter values. The procedure allows the common estimation of some of these parameters. The parameters themselves will be estimated by the maximum likelihood method; the estimators will be calculated iteratively by the Newton-Raphson method. To prove if the common estimation is possible, we propose as a suitable test the maximum likelihood ratio test. Finally we show the application of our procedure in the case of the probit analysis.     

Nonparametric estimation of the causal effect of a stochastic threshold-based intervention

Identifying a biomarker or treatment-dose threshold that marks a specified level of risk is an important problem, especially in clinical trials. In view of this goal, we consider a covariate-adjusted threshold-based interventional estimand, which happens to equal the binary treatment–specific mean estimand from the causal inference literature obtained by dichotomizing the continuous biomarker or treatment as above or below a threshold. The unadjusted version of this estimand was considered in Donovan et al.. Expanding upon Stitelman et al., we show that this estimand, under conditions, identifies the expected outcome of a stochastic intervention that sets the treatment dose of all participants above the threshold. We propose a novel nonparametric efficient estimator for the covariate-adjusted threshold-response function for the case of informative outcome missingness, which utilizes machine learning and targeted minimum-loss estimation (TMLE). We prove the estimator is efficient and characterize its asymptotic distribution and robustness properties. Construction of simultaneous 95% confidence bands for the threshold-specific estimand across a set of thresholds is discussed. In the Supporting Information, we discuss how to adjust our estimator when the biomarker is missing at random, as occurs in clinical trials with biased sampling designs, using inverse probability weighting. Efficiency and bias reduction of the proposed estimator are assessed in simulations. The methods are employed to estimate neutralizing antibody thresholds for virologically confirmed dengue risk in the CYD14 and CYD15 dengue vaccine trials.     

Maximum Likelihood Methods for Fitting the Burr Type XII Distribution to Life Test Data

This paper describes mathematical and computational methodology for estimating the parameters of the Burr Type XII distribution by the method of maximum likelihood. Expressions for the asymptotic variances and covariances of the parameter estimates are given, and the modality of the log-likelihood and conditional log-likelihood functions is analyzed. As a result of this analysis for various a priori known and unknown parameter combinations, conditions are given which guarantee that the parameter estimates obtained will, indeed, be maximum likelihood estimates. An efficient numerical method for maximizing the conditional log-likelihood function is described, and mathematical expressions are given for the various numerical approximations needed to evaluate the expressions given for the asymptotic variances and covariances of the parameter estimates. The methodology discussed is applied in a numerical example to life test data arising in a clinical setting.     

用空间直观模型是否足以从斑块占据性资料中推断集合种群的动态过程?

在集合种群的研究中,经常要根据空间占据性数据应用斑块模型来推断种群的动态过程,在保护生物学应用中,斑块占据性模型的参数估测对于阐释集合种群动态和预测种群对生境破坏的反应极为重要。我们探讨了一种广泛应用的空间直观模型——率函数模型(Incidence function model)中参数估测的不确定性问题,通过构建由50个斑块组成的网络和两个假想的已知参数的集合种群,应用模拟模型产生集合种群随时间变化的斑块占据性数据系列：即快照(snapshot)。然后,根据这些快照,应用率函数模型和最大似然法估测种群动态参数。此外,我们还给出了传统的率函数模型的一个变形,这个变形包含了目标区效应(Target area effect)：即一个斑块的占据概率不但取决于空间隔离度,也取决于斑块本身面积的大小。结果表明：根据同一个集合种群不同的快照所估测的参数可以有很大差异,一个快照得出的参数提示的是占据性强但存活率低的集合种群,而另一个快照可能反映的是一个占据性弱但存活率高的集合种群。应用传统的率函数模型于一个包含了目标区效应的集合种群,导致斑块大小相关的灭绝率参数估测的正偏差。因此,仅根据一个快照的空间占据性数据来推测集合种群的过程有很大的不确定性[动物学报49(6)：787～794,2003]。