Batch sequential designs for computer experiments

Computer models simulating a physical process are used in many areas of science. Due to the complex nature of these codes it is often necessary to approximate the code, which is typically done using a Gaussian process. In many situations the number of code runs available to build the Gaussian process approximation is limited. When the initial design is small or the underlying response surface is complicated this can lead to poor approximations of the code output. In order to improve the fit of the model, sequential design strategies must be employed. In this paper we introduce two simple distance based metrics that can be used to augment an initial design in a batch sequential manner. In addition we propose a sequential updating strategy to an orthogonal array based Latin hypercube sample. We show via various real and simulated examples that the distance metrics and the extension of the orthogonal array based Latin hypercubes work well in practice.     

On the extremal behavior of a nonstationary normal random field

Let X={_Xn}n?1$\mathbf{X}=\{X_{\mathbf{n}}{\}}_{\mathbf{n}?\mathbf{1}}$ be a nonstationary random field satisfying a long range weak dependence for each coordinate at a time and a local dependence condition that avoids clustering of exceedances of high values. For these random fields, the probability of no exceedances of high values can be approximated by exp(−τ)$\exp (-\tau )$, where τ$\tau$ is the limiting mean number of exceedances. We present a class of nonstationary normal random fields for which this result can be applied.     

An inequality for random replacement sampling plans

A sample (X₁ …, X_n) is drawn from a population of size N. Karlin (1974) conjectured that for any function ? in a certain class of real-valued functions on the sample space, ? is at least as large for sampling with replacement as for any other random replacement sampling plan. This conjecture is proved under the assumption that ?     

On prediction and mean squared error

Practical questions motivate the search for predictors either of an as yet unobserved random vector, or of a random function of a parameter. An extension of the classical UMVUE theory is presented to cover such situations. In includes a Rao-Blackwell-type theorem, a Cramer-Rao-type inequality, and necessary and sufficient conditions for a predictor to minimize the mean squared error uniformly in the parameter. Applications are considered to the problem of selected means, the species problem, and the examination of some u-v estimates of Robbins (1988).     

《子虚赋》、《上林赋》：大一统思想的文学阐释

《子虚赋》、《上林赋》以文学的形式,客观地阐释了汉代鼎盛时期走向大一统的历史必然。主要表现在赋中的"巨丽之美"的描绘和讽谕的意图,顺应了时代的要求,与董仲舒提出的大一统思想异曲同工。     

Sur l'estimation du risque encouru en reconstruisant une forme aléatoire

The problem of estimating the risk corresponding to the reconstruction of a random pattern is reviewed. It is shown that for a particular but important model, the problem is reduced to the estimation of two parameters closely related to those appearing in a two-state Markov chain, which is of independent interest. The estimation of the Markov chain's parameters is studied from the decision-theoretic point of view. Estimators which are better than others previously considered are obtained and adapted to the estimation of the corresponding risk. Examples are are analyzed; even if a very empirical method is used to give values to the parameters of an a priorilaw, some good estimators of the risk are obtained.     

Multilevel modelling of the geographical distributions of diseases

Multilevel modelling is used on problems arising from the analysis of spatially distributed health data. We use three applications to demonstrate the use of multilevel modelling in this area. The first concerns small area all-cause mortality rates from Glasgow where spatial autocorrelation between residuals is examined. The second analysis is of prostate cancer cases in Scottish counties where we use a range of models to examine whether the incidence is higher in more rural areas. The third develops a multiple-cause model in which deaths from cancer and cardiovascular disease in Glasgow are examined simultaneously in a spatial model. We discuss some of the issues surrounding the use of complex spatial models and the potential for future developments.     

The heterogeneous impact of insurance on health care demand among young adults: a panel data analysis*

Success of the recently implemented Affordable Care Act hinges on previously uninsured young adults enrolling in coverage. How will increased coverage, in turn, affect health care utilization? This paper applies variable coefficient panel models to estimate the impact of insurance on health care utilization among young adults. The econometric setup, which accommodates nonlinear usage measures, attempts to address the potential endogeneity of insurance status. The main finding is that, for approximately one-fifth of young adults, insurance does not substantially alter health care consumption. On the other hand, another one-fifth of young adults have large moral hazard effects. Among that group, insurance increases the probability of having a routine checkup by 71–120%, relative to mean probabilities, and insurance increases the number of curative-based doctor office visits by 67–181%, relative to the mean number of visits.     

缺失数据下的逆概率多重加权分位回归估计及其应用

数据缺失问题普遍存在于应用研究中。在随机缺失机制假定下,本文从模型推断角度出发,针对线性缺失分位回归模型,提出一种新的有效估计方法——逆概率多重加权（IPMW）估计。该方法是在逆概率加权（IPW）估计的基础上,结合倾向得分匹配及模型平均思想,经过多次估计,加权确定最终参数估计结果。该方法适用于响应变量是独立同分布或独立非同分布的情形,并适用于绝大多数缺失场景。经过理论推导及模拟研究发现,IPMW估计量在继承IPW估计量的优势上具有更稳健的性质。最后,将该方法应用于含有缺失数据的微观调查数据中,研究了经济较发达的准一线城市中等收入群体消费水平的影响因素,对比两种估计方法的估计结果及置信带,发现逆概率多重加权估计量的标准偏差更小,估计结果更稳健。