Likelihood Based Inference in Non-linear Regression Models Using the p* and R* Approach

We develop second order asymptotic results for likelihood-based inference in Gaussian non-linear regression models. We provide an approximation to the conditional density of the maximum likelihood estimator given an approximate ancillary statistic (the affine ancillary). From this approximation, we derive a statistic to test an hypothesis on one component of the parameter. This test statistic is an adjustment of the signed log-likelihood ratio statistic. The distributional approximations (for the maximum likelihood estimator and for the test statistic) are of second order in large deviation regions.     

Use of Cohort Information in the Design and Analysis of Case-Control Studies

Abstract.  Four case studies are presented to illustrate how information available on cohort members can be used to inform the control selection in epidemiologic case-control studies. The basic framework is the nested case-control paradigm and accompanying analysis methods. Emphasis is on development of intuition for choosing study design candidates, the form of the estimators, and extensions of the basic theory to solve design and analysis problems.     

二层多目标规划的最优性条件

本文利用［１］中所介绍的集值函数的导数和伴随导数的概念，对二层多目标决策模型的最优性，给出了两个充分条件和两个必要条件。     

Overcoming Confirmation Bias in Causal Attribution: A Case Study of Antibiotic Resistance Risks

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if‐then relations, and evidence. Decision‐related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health‐related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM‐like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.     

论中国死刑的立法限制

虽然全球多数国家已废除或停止执行死刑，但是，中国至少在社会主义初级阶段不能废除死刑。中国的死刑应该按照相关国际条约的规定，借鉴国际立法例，从适用条件、适用对象、核准程序、执行方式上进一步限制，并尽快建立相应的法律救济制度。     

Securing Passenger Aircraft from the Threat of Man‐Portable Air Defense Systems (MANPADS)

In this article, we develop a model for the expected maximum hit probability of an attack on a commercial aircraft using MANPADS, as a function of the (random) location of the attacker. We also explore the sensitivity of the expected maximum hit probability to the parameters of the model, including both attacker parameters (such as weapon characteristics) and defender parameters (such as the size of the secure region around the airport). We conclude that having a large secure region around an airport offers some protection against MANPADS, and that installing onboard countermeasures reduces the success probability of a MANPADS attack.     

Modelling asymmetric volatility dynamics by multivariate BL-GARCH models

The class of Multivariate BiLinear GARCH (MBL-GARCH) models is proposed and its statistical properties are investigated. The model can be regarded as a generalization to a multivariate setting of the univariate BL-GARCH model proposed by Storti and Vitale (Stat Methods Appl 12:19–40, 2003a; Comput Stat 18:387–400, 2003b). It is shown how MBL-GARCH models allow to account for asymmetric effects in both conditional variances and correlations. An EM algorithm for the maximum likelihood estimation of the model parameters is derived. Furthermore, in order to test for the appropriateness of the conditional variance and covariance specifications, a set of robust conditional moments test statistics are defined. Finally, the effectiveness of MBL-GARCH models in a risk management setting is assessed by means of an application to the estimation of the optimal hedge ratio in futures hedging.     

Marginally restricted sequential D‐optimal designs

In many experiments, not all explanatory variables can be controlled. When the units arise sequentially, different approaches may be used. The authors study a natural sequential procedure for “marginally restricted” D‐optimal designs. They assume that one set of explanatory variables (x₁) is observed sequentially, and that the experimenter responds by choosing an appropriate value of the explanatory variable x₂. In order to solve the sequential problem a priori, the authors consider the problem of constructing optimal designs with a prior marginal distribution for x₁. This eliminates the influence of units already observed on the next unit to be designed. They give explicit designs for various cases in which the mean response follows a linear regression model; they also consider a case study with a nonlinear logistic response. They find that the optimal strategy often consists of randomizing the assignment of the values of x₂.     

Focused Information Criterion for Capture–Recapture Models for Closed Populations

Abstract. We propose a criterion for selecting a capture–recapture model for closed populations, which follows the basic idea of the focused information criterion (FIC) of Claeskens and Hjort. The proposed criterion aims at selecting the model which, among the available models, leads to the smallest mean‐squared error (MSE) of the resulting estimator of the population size and is based on an index which, up to a constant term, is equal to the asymptotic MSE of the estimator. Two alternative approaches to estimate this FIC index are proposed. We also deal with multimodel inference; in this case, the population size is estimated by using a weighted average of the estimates coming from different models, with weights chosen so as to minimize the MSE of the resulting estimator. The proposed model selection approach is compared with more common approaches through a series of simulations. It is also illustrated by an application based on a dataset coming from a live‐trapping experiment.     

食品包装配重中的一类数学问题

配重是食品包装中的一个不可缺少的环节。目前,生产实际当中亟待解决的问题是如何提高配重的效率和质量精度,而配重首先要解决的问题是随机变量自身条件分布的问题。本文运用随机数学的方法,给出了离散型随机变量的条件分布律和连续型随机变量的条件概率密度,从而解决了食品包装中的相应问题。