Yearly variation in recruitment and its effect on population dynamics inYoldia notabilis (Mollusca: Bivalvia), analyzed using projection matrix model

Summary Long-term variation in recruitment was estimated by constructing projection matrices for a marine bivalve,Yoldia notabilis, at two stations in Otsuchi Bay, northeastern Japan, and the effects of its variation on population dynamics were examined using a simple matrix model. The matrix model was developed from the Leslie matrix, in which the population growth rate λ was expressed as a function of recruitment rater ₀. The equilibrium recruitment rater _s, or the recruitment rate required to maintain population at constant size (λ=1), was expressed by the reciprocal of the reproductive value of a newly recruited individual. The estimates ofr _s for the field population were lower at the shallower station than at the deeper station, reflecting higher survivorship and fecundity. Past recruitment rate estimated both by the field samplings for 3 years and by the back-calculation from the current age structure for over 10 years showed large yearly variation, ranging between 0 and 58.6×10⁻⁴. The estimates were larger thanr _s, and hence, large enough to increase population size (λ>1) only in approximately one-third of the estimated years. This suggests that the population has been maintained by occasional successful recruitment occurring once every few years.     

日本唯美主义文学在中国现代文坛的变异

日本唯美主义文学对中国现代文学产生了深远的影响。然而,中国作家在接受其影响的同时,表现出一种与日本不同的选择性。其一,两者所提倡的“为艺术而艺术”的内涵不同。其二,两者所体现的颓废不同。中国传统文化的根基、中日文化的差异和20世纪二三十年代中国社会现实是产生这种变异现象的原因。     

杜鲁门·卡波特小说的故事空间变化

美国作家杜鲁门·卡波特擅长将叙事策略中的故事空间变化应用于自己的小说中。本文分析了他运用的三种故事空间变化的方式:南方小镇与城市之间的故事空间变化、逃亡式的故事空间变化和旅途式的故事空间变化。以上三种叙事方式不仅仅被视为叙事策略,亦表现出卡波特对美国社会的关注和对现实的深入思考,具有丰富的内蕴。这不失为卡波特研究的一种新思路。     

Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression

The monitoring of process/product profiles is presently a growing and promising area of research in statistical process control. This study is aimed at developing monitoring schemes for nonlinear profiles with random effects. We utilize the technique of principal components analysis to analyze the covariance structure of the profiles and propose monitoring schemes based on principal component (PC) scores. The number of the PC scores used in constructing control charts is crucial to the detecting power. In the Phase I analysis of historical data, due to the dependency of the PC-scores, we adopt the usual Hotelling T ² chart to check the stability. For Phase II monitoring, we study individual PC-score control charts, a combined chart scheme that combines all the PC-score charts, and a T ² chart. Although an individual PC-score chart may be perfect for monitoring a particular mode of variation, a chart that can detect general shifts, such as the T ² chart and the combined chart scheme, is more feasible in practice. The performances of the schemes under study are evaluated in terms of the average run length.     

A General Model of Random Variation

A statistical distribution of a random variable is uniquely represented by its normal-based quantile function. For a symmetrical distribution it is S-shaped (for negative kurtosis) and inverted S-shaped (otherwise). As skewness departs from zero, the quantile function gradually transforms into a monotone convex function (positive skewness) or concave function (otherwise). Recently, a new general modeling platform has been introduced, response modeling methodology, which delivers good representation to monotone convex relationships due to its unique “continuous monotone convexity” property. In this article, this property is exploited to model the normal-based quantile function, and explored using a set of 27 distributions.     

First- and Second-order Asymptotics for the Tail Distortion Risk Measure of Extreme Risks

The tail distortion risk measure at level p was first introduced in Zhu and Li (2012 Zhu, L., Li, H. (2012). Tail distortion risk and its asymptotic analysis. Insur. Math. Econ. 51(1):115–121.[Crossref], [Web of Science ®] , [Google Scholar]), where the parameter p ∈ (0, 1) indicates the confidence level. They established first-order asymptotics for this risk measure, as p↑1, for the Fréchet case. In this article, we extend their work by establishing both first-order and second-order asymptotics for the Fréchet, Weibull, and Gumbel cases. Numerical studies are also carried out to examine the accuracy of both asymptotics.     

Maximum likelihood estimation,from doubly censored samples,of the mean of a normal distribution with known coeffucient of variation

Let x be a random variable having the normal distribution with mean μ and variance c²μ², where c is a known constant. The maximum likelihood estimation of μ when the lowest r₁ and the highest r₂ sample values censored have been given the asymptotic variance of the maximum likelihood estimator is obtained.     

Asymptotically minimax non–parametric function estimation with positivity constraints I

The spatially inhomogeneous smoothness of the non-parametric density or regression-function to be estimated by non-parametric methods is often modelled by Besov- and Triebel-type smoothness constraints. For such problems, Donoho and Johnstone [D.L. Donoho and I.M. Johnstone, Minimax estimation via wavelet shrinkage. Ann. Stat. 26 (1998), pp. 879–921.], Delyon and Juditsky [B. Delyon and A. Juditsky, On minimax wavelet estimators, Appl. Comput. Harmon. Anal. 3 (1996), pp. 215–228.] studied minimax rates of convergence for wavelet estimators with thresholding, while Lepski et al. [O.V. Lepski, E. Mammen, and V.G. Spokoiny, Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimators with variable bandwidth selectors, Ann. Stat. 25 (1997), pp. 929–947.] proposed a variable bandwidth selection for kernel estimators that achieved optimal rates over Besov classes. However, a second challenge in many real applications of non-parametric curve estimation is that the function must be positive. Here, we show how to construct estimators under positivity constraints that satisfy these constraints and also achieve minimax rates over the appropriate smoothness class.     

Indications for,and timing of,planned caesarean section: A systematic analysis of clinical guidelines

BackgroundThere has been a worldwide rise in planned caesarean sections over recent decades, with significant variations in practice between hospitals and countries. Guidelines are known to influence clinical decision-making and, potentially, unwarranted clinical variation. The aim of this study was to review guidelines for recommendations in relation to the timing and indications for planned caesarean section as well as recommendations around the process of decision-making.MethodA systematic search of national and international English-language guidelines published between 2008 and 2018 was undertaken. Guidelines were reviewed, assessed in terms of quality and extracted independently by two reviewers.FindingsIn total, 49 guidelines of varying quality were included. There was consistency between the guidelines in potential indications for caesarean section, although guidelines vary in terms of the level of detail. There was substantial variation in timing of birth, for example recommended timing of caesarean section for women with uncomplicated placenta praevia is between 36 and 39 weeks depending on the guideline. Only 11 guidelines provided detailed guidance on shared decision-making. In general, national-level guidelines from Australia, and overseas, received higher quality ratings than regional guidelines.ConclusionThe majority of guidelines, regardless of their quality, provide very limited information to guide shared decision-making or the timing of planned caesarean section, two of the most vital aspects of guidance. National guidelines were generally of better quality than regional ones, suggesting these should be used as a template where possible and emphasis placed on improving national guidelines and minimising intra-country, regional, variability of guidelines.     

The (non) gradual association of popularity with peer‐nominated and observed behavior in a cooperative and competitive context

Popularity among peers might be related to behavior in gradual or non‐gradual ways. In this research, a popularity subgroups approach was used to examine whether some behaviors were associated with only specific levels of popularity. Moreover, observational data in popularity research is valuable yet scarce. This research, therefore, also examined the association between popularity and observed behavior in a cooperative and competitive setting. In total, 182 early adolescents (58.2% girls, M_age = 10.7 years) completed peer nominations and were observed during a cooperative and a competitive task in groups of four. Results show that affective ties increased gradually with increasing popularity, but that relational aggression, bullying and attention‐attracting qualities distinguished popular adolescents from other early adolescents, and victimization distinguished unpopular adolescents from the other early adolescents. Observations showed that high popularity was defined by high levels of negative behavior, prosocial resource control, skillful leadership, and influence, with the effect of popularity on influence being stronger in the cooperative than the competitive setting. Using multiple methods, and taking context into account, a more complete behavioral profile of different levels of popularity is provided.