论附条件不起诉之监督机制——以刑事诉讼法基本原则为视角

刑事诉讼法原则是贯穿于整个刑事诉讼的指导思想,在附条件不起诉的监督制度中,互相制约、严格遵守法定程序等基本原则都所有体现。但因现行附条件不起诉监督制度存在不足,导致了部分刑事诉讼法原则在附条件不起诉监督制度适用还存在很大的空间。诸如,在附条件不起诉决定程序,完善互相制约原则的适用,构建法院司法审查权提前介入制度;完善严格遵守法律程序原则的适用,确立程序违法的附条件不起诉决定撤销制度;在附条件不起诉救济程序中,完善检察权专属于检察机关行使原则,赋予检察机关对不法的附条件不起诉决定的撤销权。此外,严格的惩戒制度也是遏制不起诉裁量权滥用的重要力量。     

Measures of biomarker dependence using a copula-based multivariate epsilon–skew–normal family of distributions

In this note we develop a new multivariate copula model based on epsilon–skew–normal marginal densities for the purpose of examining biomarker dependency structures. We illustrate the flexibility and utility of this model via a variety of graphical tools and a data analysis example pertaining to salivary biomarker. The multivariate normal model is a sub-model of the multivariate epsilon–skew–normal distribution.     

A Model Specification Test For GARCH(1,1) Processes

We provide a consistent specification test for generalized autoregressive conditional heteroscedastic (GARCH (1,1)) models based on a test statistic of Cramér‐von Mises type. Because the limit distribution of the test statistic under the null hypothesis depends on unknown quantities in a complicated manner, we propose a model‐based (semiparametric) bootstrap method to approximate critical values of the test and to verify its asymptotic validity. Finally, we illuminate the finite sample behaviour of the test by some simulations.     

Uniform Ergodicity of the Particle Gibbs Sampler

The particle Gibbs sampler is a systematic way of using a particle filter within Markov chain Monte Carlo. This results in an off‐the‐shelf Markov kernel on the space of state trajectories, which can be used to simulate from the full joint smoothing distribution for a state space model in a Markov chain Monte Carlo scheme. We show that the particle Gibbs Markov kernel is uniformly ergodic under rather general assumptions, which we will carefully review and discuss. In particular, we provide an explicit rate of convergence, which reveals that (i) for fixed number of data points, the convergence rate can be made arbitrarily good by increasing the number of particles and (ii) under general mixing assumptions, the convergence rate can be kept constant by increasing the number of particles superlinearly with the number of observations. We illustrate the applicability of our result by studying in detail a common stochastic volatility model with a non‐compact state space.     

The Extensively Corrected Score for Measurement Error Models

In measurement error problems, two major and consistent estimation methods are the conditional score and the corrected score. They are functional methods that require no parametric assumptions on mismeasured covariates. The conditional score requires that a suitable sufficient statistic for the mismeasured covariate can be found, while the corrected score requires that the object score function can be estimated without bias. These assumptions limit their ranges of applications. The extensively corrected score proposed here is an extension of the corrected score. It yields consistent estimations in many cases when neither the conditional score nor the corrected score is feasible. We demonstrate its constructions in generalized linear models and the Cox proportional hazards model, assess its performances by simulation studies and illustrate its implementations by two real examples.     

Instrument Assisted Regression for Errors in Variables Models with Binary Response

We study errors‐in‐variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.     

中国人均SO2排放的省际收敛性——基于面板数据的实证分析

随着中国经济持续快速发展,中国的环境污染问题也日益突出。作为一种常规污染物,SO2的排放长期受到中国政府的高度重视。采集1992—2012年中国30个省市的人均SO2排放量,运用静态与动态面板相结合的回归方式,对于省际人均SO2排放量的相对与绝对收敛性进行研究,对影响人均SO2排放收敛的因素进行分析。结果表明: 在总样本期上,省际人均SO2排放量存在着绝对与相对收敛。相对于2001年以后的时期,2001年以前的省际人均SO2排放量收敛速度更快。对于收敛速度的影响因素,二次产业比重与中国SO2排放具有显著的正相关关系,更高的二次产业占GDP比重会降低收敛速度。而各省人均实际GDP与人口密度则对人均SO2的排放量有负向影响,更高的人均收入和人口密度会使人均SO2排放收敛速度加快。     

Conditional logistic regression in cluster-specific 1: m matched treatment–control designs

Conditional logistic regression is a popular method for estimating a treatment effect while eliminating cluster-specific nuisance parameters when they are not of interest. Under a cluster-specific 1: m matched treatment–control study design, we present a new closed-form relationship between the conditional logistic regression estimator and the ordinary logistic regression estimator. In addition, we prove an equivalence between the ordinary logistic regression and the conditional logistic regression estimators, when the clusters are replicated infinitely often, which indicates that potential bias concerns when applying conditional logistic regression to complex survey samples.