Hard thresholding regression

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ?₀‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology.     

A Narrative Approach to Pregnancy‐related Discrimination and Leader–follower Relationships

This study sheds light on women's perceptions of pregnancy‐related discrimination committed by the discriminated woman's immediate leader. The purpose is to show how women describe this perception during pregnancy and after their return to working life after maternity leave. Further, the study explores the ways in which the quality of the leader–follower relationship is revealed during the narration process. The research draws on five individual stories, collected in two rounds of interviews. Representations of several kinds of discriminatory practices can be found in the narratives and reveal that even being a partner in a good‐quality leader–follower relationship does not necessarily protect a woman from pregnancy‐related discrimination. However, women involved in a high‐quality leader–follower relationship described their perceptions in a more positive manner than those in a low‐quality relationship. The contribution of the article to the literature is twofold: firstly, the results challenge the traditional view that high‐quality leader–member exchange relationships lead to positive outcomes. Secondly, examining the different ways in which people describe their perceptions of discrimination deepens our understanding of the equality problems that working women face.     

On the asymptotic normality of the R-estimators of the slope parameters of simple linear regression models with associated errors

ABSTRACT

The purpose of this paper is to prove, under mild conditions, the asymptotic normality of the rank estimator of the slope parameter of a simple linear regression model with stationary associated errors. This result follows from a uniform linearity property for linear rank statistics that we establish under general conditions on the dependence of the errors. We prove also a tightness criterion for weighted empirical process constructed from associated triangular arrays. This criterion is needed for the proofs which are based on that of Koul [Behavior of robust estimators in the regression model with dependent errors. Ann Stat. 1977;5(4):681–699] and of Louhichi [Louhichi S. Weak convergence for empirical processes of associated sequences. Ann Inst Henri Poincaré Probabilités Statist. 2000;36(5):547–567].     

Monotonic change point estimation of generalized linear model-based regression profiles

In this article, a maximum likelihood estimator is derived in the generalized linear model-based regression profiles under monotonic change in Phase II. The performance of the proposed estimator is comprehensively investigated through some special cases, and compared with estimators under step change and drift. The results show that the proposed estimator has better performance in small and medium shifts under different increasing changes. Finally, the applicability of the proposed estimator is illustrated using a real case.     

Marginal likelihood estimation from the Metropolis output: tips and tricks for efficient implementation in generalized linear latent variable models

The marginal likelihood can be notoriously difficult to compute, and particularly so in high-dimensional problems. Chib and Jeliazkov employed the local reversibility of the Metropolis–Hastings algorithm to construct an estimator in models where full conditional densities are not available analytically. The estimator is free of distributional assumptions and is directly linked to the simulation algorithm. However, it generally requires a sequence of reduced Markov chain Monte Carlo runs which makes the method computationally demanding especially in cases when the parameter space is large. In this article, we study the implementation of this estimator on latent variable models which embed independence of the responses to the observables given the latent variables (conditional or local independence). This property is employed in the construction of a multi-block Metropolis-within-Gibbs algorithm that allows to compute the estimator in a single run, regardless of the dimensionality of the parameter space. The counterpart one-block algorithm is also considered here, by pointing out the difference between the two approaches. The paper closes with the illustration of the estimator in simulated and real-life data sets.     

美国社会家庭婚姻之问题——析《这年的秋天》的叙事艺术

欧茨早期的代表作《这年的秋天》叙事艺术高超,展现了她对于美国现代家庭婚姻问题的思索。小说的叙事结构明显,欧茨从过去和现在两个层面综合展现了埃莉诺从年轻时到年老时完整的生活历程,使读者了解到一段婚外情对埃莉诺生活的影响,透过这些人物形象揭示出20世纪中后期美国社会存在的婚姻危机。     

Bandwidth selection through cross-validation for semi-parametric varying-coefficient partially linear models

We study bandwidth selection for a class of semi-parametric models. The proper choice of optimal bandwidth minimizes the prediction errors of the model. We provide detailed derivation of our procedure and the corresponding computation algorithms. Our proposed method simplifies the computation of the cross-validation criteria and facilitates more complicated inference and analysis in practice. A data set from Wisconsin Diabetes Registry has been analysed as an illustration.     

《寻隐者不遇》的叙事翻译补偿

以《寻隐者不遇》为例，从叙事视角、叙事方式、叙事结构、叙事策略和叙事意象等方面来考查原文的叙事要素和叙事特征，在对比WitterBynner，BurtonWatson，吴钧陶，王大濂，孙大雨，万昌盛、王倘中，许渊冲等译者的七个英译文本叙事翻译质量的基础上，进行叙事翻译补偿。     

Results concerning the generalized partially linear single-index model

The central topic of this article is the estimation of parameters of the generalized partially linear single-index model (GPLSIM). Two numerical optimization procedures are presented and an S-plus program based on these procedures is compared to a program by Wand in a simulation setting. The results from these simulations indicate that the estimates for the new procedures are as good, if not better, than Wand's. Also, this program is much more flexible than Wand's since it can handle more general models. Other simulations are also conducted. The first compares the effects of using linear interpolation versus spline interpolation in an optimization procedure. The results indicate that by using spline interpolation one gets more stable estimates at a cost of increased computational time. A second simulation was conducted to assess the performance of a method for estimating the variance of alpha. A third set of simulations is carried out to determine the best criterion for testing that one of the elements of alpha is equal to zero. The GPLSIM is applied to a water quality data set and the results indicate an interesting relationship between gastrointestinal illness and turbidity (cloudiness) of drinking water.