局部平稳部分线性面板数据模型的统计推断

部分线性模型是一类非常重要的半参数回归模型,由于它既含有参数部分又含有非参数部分,与常规的线性模型相比具有更强的适应性和解释能力。文章研究带有局部平稳协变量的固定效应部分线性面板数据模型的统计推断。首先提出一个两阶段估计方法得到模型中未知参数和非参数函数的估计,并证明估计量的渐近性质,然后运用不变原理构造出非参数函数的一致置信带,最后通过数值模拟研究和实例分析验证了该方法的有效性。     

Re‐framing the Analysis: A 3‐dimensional Perspective of Prisoners’ Children's Well‐being

This article highlights three dimensions to understanding children's well‐being during and after parental imprisonment which have not been fully explored in current research. A consideration of ‘time’ reveals the importance of children's past experiences and their anticipated futures. A focus on ‘space’ highlights the impact of new or altered environmental dynamics. A study of ‘agency’ illuminates how children cope within structural, material and social confines which intensify vulnerability and dependency. This integrated perspective reveals important differences in individual children's experiences and commonalities in broader systemic and social constraints on prisoners’ children. The paper analyses data from a prospective longitudinal study of 35 prisoners’ children during and after their (step) father's imprisonment to illustrate the arguments.     

Testing equality of two normal covariance matrices with monotone missing data

Abstract

The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

川东北地区须家河组二段储层差异性分析

随着勘探程度的深入，为了扩大四川盆地天然气勘探领域、寻找新的勘探接替区，四川盆地的勘探区域由盆内逐渐向盆缘延伸。在这种背景下，川东北地区须家河组二段相继取得新的勘探成果，但在勘探开发过程中逐渐暴露了诸如区块之间气井产能差异大，储层的碎屑组分、结构及厚度、物性、孔隙结构差异明显等问题，制约了勘探开发的步伐。针对上述问题，根据岩芯观察描述、分析化验资料及测井解释成果，从沉积、成岩方面入手，对比剖析了川东北龙岗和营山地区须二段储层特征的差异性及其成因，认为远离物源区和高能水体的反复淘洗是造成储层差异性的首要因素，强烈的压实作用和多期石英加大对储层差异性起关键作用，自生绿泥石衬边胶结和长石选择性溶蚀最终决定了储层差异性。从而解释了气藏勘探开发中遇到的诸多问题，对有效指导该区的下一步勘探开发工作具有重要意义。     

Design selection for strong orthogonal arrays

Strong orthogonal arrays (SOAs) were recently introduced and studied as a class of space‐filling designs for computer experiments. An important problem that has not been addressed in the literature is that of design selection for such arrays. In this article, we conduct a systematic investigation into this problem, and we focus on the most useful SOA(n,m,4,2 + )s and SOA(n,m,4,2)s. This article first addresses the problem of design selection for SOAs of strength 2+ by examining their three‐dimensional projections. Both theoretical and computational results are presented. When SOAs of strength 2+ do not exist, we formulate a general framework for the selection of SOAs of strength 2 by looking at their two‐dimensional projections. The approach is fruitful, as it is applicable when SOAs of strength 2+ do not exist and it gives rise to them when they do. The Canadian Journal of Statistics 47: 302–314; 2019 © 2019 Statistical Society of Canada     

Properties of the weighted log‐rank test in the design of confirmatory studies with delayed effects

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log‐rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type‐I error rate of the of the weighted log‐rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.     

A Bayesian Analysis of Abundance,Trend, and Population Viability for Harbor Seals in Iliamna Lake,Alaska

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts. Underlying models of abundance and trend were based on a Leslie matrix model that used prior information on vital rates from the literature. We developed three scenarios for variability in the priors and used them as part of a sensitivity analysis. The models were fitted using Markov chain Monte Carlo methods. The population production rate implied by the vital rate estimates was about 5% per year, very similar to the average annual harvest rate. After a period of growth in the 1980s, the population appears to be relatively stable at around 400 individuals. A population viability analysis assessing the risk of quasi‐extinction, defined as any reduction to 50 animals or below in the next 100 years, ranged from 1% to 3%, depending on the prior scenario. Although this is moderately low risk, it does not include genetic or catastrophic environmental events, which may have occurred to the population in the past, so our results should be applied cautiously.     

伸展构造背景下倾角资料的构造解释

系统总结了在伸展构造背景下，导致反映构造特征的地层倾角模式复杂化的多种因素。沉积相的不同、岩性变化、井眼状况和测井作业时间等多种因素都可能使地层倾角资料复杂化。进一步指出在拉张盆地中，进行倾角资料的构造解释，必须了解其构造样式的多样性和复杂性，解释存在的多解性。重点解剖了铲式断层在横剖面情况下，对于不同构造位置下的倾角模式。详细分析了一个倾角模式对应多种构造样式的倾角构造解释实例。倾角资料的构造解释应是综合解释，既要综合井筒的相关地质资料，又要综合测井与地质知识，还必须考虑综合地震资料。     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.