Estimation of Hazard, Density and Survivor Functions for Randomly Censored Data

Maximum likelihood estimation and goodness-of-fit techniques are used within a competing risks framework to obtain maximum likelihood estimates of hazard, density, and survivor functions for randomly right-censored variables. Goodness-of- fit techniques are used to fit distributions to the crude lifetimes, which are used to obtain an estimate of the hazard function, which, in turn, is used to construct the survivor and density functions of the net lifetime of the variable of interest. If only one of the crude lifetimes can be adequately characterized by a parametric model, then semi-parametric estimates may be obtained using a maximum likelihood estimate of one crude lifetime and the empirical distribution function of the other. Simulation studies show that the survivor function estimates from crude lifetimes compare favourably with those given by the product-limit estimator when crude lifetimes are chosen correctly. Other advantages are discussed.     

The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes)

Conservation biology aims at assessing the status of a population, based on information which is often incomplete. Integrated population modelling based on state‐space models appears to be a powerful and relevant way of combining into a single likelihood several types of information such as capture‐recapture data and population surveys. In this paper, the authors describe the principles of integrated population modelling and they evaluate its performance for conservation biology based on a case study, that of the black‐footed albatross, a northern Pacific albatross species suspected to be impacted by longline fishing     

Robust testing with generalized partial linear models for longitudinal data

By approximating the nonparametric component using a regression spline in generalized partial linear models (GPLM), robust generalized estimating equations (GEE), involving bounded score function and leverage-based weighting function, can be used to estimate the regression parameters in GPLM robustly for longitudinal data or clustered data. In this paper, score test statistics are proposed for testing the regression parameters with robustness, and their asymptotic distributions under the null hypothesis and a class of local alternative hypotheses are studied. The proposed score tests reply on the estimation of a smaller model without the testing parameters involved, and perform well in the simulation studies and real data analysis conducted in this paper.     

微生物杀虫剂的开发和使用现状

微生物源农药包括活体微生物农药和农用抗生素两大类.前者主要包括Bt制剂、病毒杀虫剂、真菌杀虫剂和真菌除草剂;后者主要指微生物所产生的一些有活性的次级代谢产物及其化学修饰物.随着越来越多的化学农药被限制或禁止使用,微生物农药作为目前应用最多的一大类生物农药有着极为广阔的应用前景.微生物杀虫剂是目前微生物农药产业的重要组成部分.本文综述了杀虫细菌,杀虫真菌,昆虫病毒三个部分,及其近年国内外的研究和应用开发状况,同时对其发展前景进行了展望.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

高校全程就业指导模式构建研究

目前高校就业指导现状已不适应高等教育和社会发展的需要。本文以心理测量指导、职业生涯设计指导、学业指导、择业指导、创业指导、升学指导六大指导为主线建构全程化的就业指导模式,全程就业指导就是从大学一年级开始,根据职业指导理论和大学生发展阶段,对大学生分年级、分阶段,结合学生共性与个性,提供全程化就业指导,以实现双向选择、人职匹配和职业目标。     

具体应用中的矛盾分析法之种种

矛盾分析法在科学认识中的具体应用,可作多侧面的展开,形成多方面的更为具体的科学方法。这些具体科学方法可概括为,矛盾的定性分析法、矛盾的定位分析法、矛盾的定点分析法、矛盾的定量分析法、矛盾的定势分析法、矛盾的历史过程分析法、矛盾的逻辑过程分析法、矛盾的系统分析法、矛盾的对立面互补分析法等等.它们均体现哲学方法与具体科学方法的一致性。     

英语否定词在语言表达中的应用

否定词的用法对学习英语的学生来说一直是个难题。因为在英语中否定词的用法和意义是灵活多变的。本文主要从四个方面阐述了英语否定词在语言表达中的具体应用。旨在帮助学生更有效 ,灵活地理解和使用。