试析武则天时期的劝农与科责

武则天时期的劝农性科责无论对象还是惩罚的力度都与中国古代历史上通常的劝农形式有别。究其根源，一方面是由于武则天将劝农与官吏考课联系起来，并辅之以严厉的监察，促使地方官员进一步向基层吏员和百姓施压，从而导致严厉的科罚。另一方面，武则天时期出现大量浮户，劝农与科责既是在民户逃亡后保证农业生产继续的手段，也是巩固劝农政绩、招徕户口归农的措施。武则天时期的劝农性科责从某种程度上保证了这一阶段农业经济的发展，但政府的“劝农”本质最终还是促使劝农官与基层农业生产管理日益疏离，劝农愈趋形式化。     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.     

Model selection consistency of U-statistics with convex loss and weighted lasso penalty

In the paper we consider minimisation of U-statistics with the weighted Lasso penalty and investigate their asymptotic properties in model selection and estimation. We prove that the use of appropriate weights in the penalty leads to the procedure that behaves like the oracle that knows the true model in advance, i.e. it is model selection consistent and estimates nonzero parameters with the standard rate. For the unweighted Lasso penalty, we obtain sufficient and necessary conditions for model selection consistency of estimators. The obtained results strongly based on the convexity of the loss function that is the main assumption of the paper. Our theorems can be applied to the ranking problem as well as generalised regression models. Thus, using U-statistics we can study more complex models (better describing real problems) than usually investigated linear or generalised linear models.     

程序正义理念下的死刑复核问题探析

受我国传统刑事诉讼价值取向的影响,现行死刑复核程序在实践运行中一直存在着很多问题,大大影响到死刑复核程序功能的实现。死刑核准权统一收归最高人民法院行使,既让我们在死刑复核问题上看到了希望,又给我们提出了新的研究课题。虽然近期最高人民法院及相关部门颁布了一系列指导死刑复核程序运作的相关规定,但从实际操作效果来看,仍有一些问题需要进一步明确。     

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.     

A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors

This article considers a nonparametric additive seemingly unrelated regression model with autoregressive errors, and develops estimation and inference procedures for this model. Our proposed method first estimates the unknown functions by combining polynomial spline series approximations with least squares, and then uses the fitted residuals together with the smoothly clipped absolute deviation (SCAD) penalty to identify the error structure and estimate the unknown autoregressive coefficients. Based on the polynomial spline series estimator and the fitted error structure, a two-stage local polynomial improved estimator for the unknown functions of the mean is further developed. Our procedure applies a prewhitening transformation of the dependent variable, and also takes into account the contemporaneous correlations across equations. We show that the resulting estimator possesses an oracle property, and is asymptotically more efficient than estimators that neglect the autocorrelation and/or contemporaneous correlations of errors. We investigate the small sample properties of the proposed procedure in a simulation study.     

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.     

略论中国古代法律的基本性质——兼谈中国古代无民法

关于中国古代法律,"诸法合体,民刑不分"的习惯说法并不妥当,它意味着诸法都有,没有分开而已;民法之说则似是而非,模糊了民法与刑法的区别,也模糊了古今与中西法律的基本区别.在中国古代特定的社会历史条件下,法律作为维护家天下统治秩序的工具,核心是一个"刑"字,是泛化的刑罚.它无所谓民事与刑事的区分,只有事之大小、刑之轻重,故与民法无关,也不等同于刑法,勉强可视为一种片面的刑法.实际上,中国古代社会无从产生民法,民间社会关系与纠纷概由律及礼、俗调节.     

论刑罚资源的有效配置

对刑罚意义的不同理解以及对刑罚目的的不同追求直接决定着刑事制裁方式的选择。刑罚的目的是通过加重犯罪所需要的成本来制止犯罪,从而得到最佳的刑罚效益。在刑罚资源有限的前提下,如何选择有效的刑事制裁方式,以最合理、最小量的刑罚成本投入,最大限度获取刑罚效益,始终是一个难题。从刑罚经济分析的角度,研究犯罪与刑罚的关系,有助于实现刑罚资源的有效配置,促进刑事司法活动成本减少、效益增大,进而更好地惩治犯罪与预防犯罪。