A Necessary Condition for the Strong Oracle Property

A problem of using a non‐convex penalty for sparse regression is that there are multiple local minima of the penalized sum of squared residuals, and it is not known which one is a good estimator. The aim of this paper is to give a guide to design a non‐convex penalty that has the strong oracle property. Here, the strong oracle property means that the oracle estimator is the unique local minimum of the objective function. We summarize three definitions of the oracle property – the global, weak and strong oracle properties. Then, we give sufficient conditions for the weak oracle property, which means that the oracle estimator becomes a local minimum. We give an example of non‐convex penalties that possess the weak oracle property but not the strong oracle property. Finally, we give a necessary condition for the strong oracle property.     

Lassoing the Determinants of Retirement

This article uses Danish register data to explain the retirement decision of workers in 1990 and 1998. Many variables might be conjectured to influence this decision such as demographic, socioeconomic, financial, and health related variables as well as all the same factors for the spouse in case the individual is married. In total, we have access to 399 individual specific variables that all could potentially impact the retirement decision. We use variants of the least absolute shrinkage and selection operator (Lasso) and the adaptive Lasso applied to logistic regression in order to uncover determinants of the retirement decision. To the best of our knowledge, this is the first application of these estimators in microeconometrics to a problem of this type and scale. Furthermore, we investigate whether the factors influencing the retirement decision are stable over time, gender, and marital status. It is found that this is the case for core variables such as age, income, wealth, and general health. We also point out the most important differences between these groups and explain why these might be present.     

Variable selection in additive quantile regression using nonconcave penalty

This paper considers variable selection in additive quantile regression based on group smoothly clipped absolute deviation (gSCAD) penalty. Although shrinkage variable selection in additive models with least-squares loss has been well studied, quantile regression is sufficiently different from mean regression to deserve a separate treatment. It is shown that the gSCAD estimator can correctly identify the significant components and at the same time maintain the usual convergence rates in estimation. Simulation studies are used to illustrate our method.     

清至民国时期的东北民神

民神信仰是民俗文化的重要组成部分,东北地区由于特殊的历史和地理背景,在其民神形成的历史过程中,既有与全国民神共同的特点,也有自身的地城特征.     

改革开放前我国教育财政体制的演变与特征

建国后,在计划经济体制下,我国教育财政体制模式的选择与演变经历了“统收统支、三级管理”和“条块结合、以块为主”两个阶段,形成了中央集权的国家教育财政体制。这种体制的特征主要有:一是统收统支、高度集中;二是直接管理、年度平衡;三是完全指令、条块分割;四是两条腿走路,多渠道筹资。这种体制既推动了当时教育的发展,又日益显露弊端,这一时期教育财政制度安排的种种尝试,为改革开放后我国教育财政体制改革提供了宝贵的经验。     

Moving forward the dialogic theory of public relations: Concepts,methods and applications of organization-public dialogue

Advanced information and communication technologies and social media (Web 2.0) have significantly shaped every aspect of contemporary society since Kent and Taylor’s (1998) proposal of dialogic principles, which later evolved into dialogic theory of public relations. It is now time to move the theory forward. The special section aims to advance the dialogic theory of public relations by reviewing the scholarship in organization-public dialogue to pinpoint critical issues for its development and introducing studies that take the dialogue approach to examine a range of public relations practices in China. In this introduction, we first identify critical issues to be addressed for the development of the dialogic theory of public relations and then introduce the articles included in the section. We conclude by proposing research directions for the theoretical and practical development of the dialogic approach to public relations.     

The red and the black: China’s social credit experiment as a total test environment

China's social credit system is an unusually explicit case where technology is used by multiple actors to turn human behavior into a test object on behalf of the state's goal of modifying the larger social environment, making it an intriguing setting for thinking about the new sociology of testing. This article considers how China's search for a usable “credit” score to both allocate financial resources and explicitly measure a citizen's trustworthiness creates an emergent experimental system of governance similar to, yet not quite captured by, the kinds of experimental processes observed in literature on the platform as a form of market-based governance. As a site where “seeing like a state” and “seeing like a market” converge, the social credit system is a vantage point for observing the changing relationship between moral and economic domains in an era of digital platforms. The article highlights the experimental quality of the system and its emerging system of governance structured around reward and punishment and argues that strategic ambiguity, institutionalized through the affordances of digital platforms, is an important part of the design of this large-scale social experiment.     

Variance estimation based on blocked 3×2 cross-validation in high-dimensional linear regression

In high-dimensional linear regression, the dimension of variables is always greater than the sample size. In this situation, the traditional variance estimation technique based on ordinary least squares constantly exhibits a high bias even under sparsity assumption. One of the major reasons is the high spurious correlation between unobserved realized noise and several predictors. To alleviate this problem, a refitted cross-validation (RCV) method has been proposed in the literature. However, for a complicated model, the RCV exhibits a lower probability that the selected model includes the true model in case of finite samples. This phenomenon may easily result in a large bias of variance estimation. Thus, a model selection method based on the ranks of the frequency of occurrences in six votes from a blocked 3×2 cross-validation is proposed in this study. The proposed method has a considerably larger probability of including the true model in practice than the RCV method. The variance estimation obtained using the model selected by the proposed method also shows a lower bias and a smaller variance. Furthermore, theoretical analysis proves the asymptotic normality property of the proposed variance estimation.     

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.