刑事自诉案件证明标准的反思与重构

刑事自诉是刑法公法化之前人类社会纠纷解决的普遍形态,尽管适用范围被逐渐限缩,但仍有其独特的制度理性和价值。在立法层面,虽然自诉制度同公诉制度有诸多差异,但由于对刑事自诉制度缘起及社会功能的认知模糊,当前的刑事自诉案件证明标准在制度设计上与公诉案件混同,以致实践中自诉案件无罪率畸高,未能发挥自诉制度的实效而使该制度虚置,即为制约自诉制度功效的最大桎梏。为此,应当澄清自诉制度的本质,还原其作为"私犯"救济的历史真相和社会属性,基于提高诉讼效益和自诉人举证能力偏弱的现实,剔除公诉案件证明标准中对公权力进行限制的制度设计理念,重新建构不同于公诉案件的自诉案件证明标准,在语言表述上可以将其界定为"证据基本确实、充分,事实基本确信",以此推进刑事自诉的实行化。     

Information in experiments and sufficiency

We define measures of information contained in an experiment which are by-products of the parametric measures of Fisher, Vajda, Mathai and Boekee and the non-parametric measures of Bhattacharyya, Rényi, Matusita, Kagan and Csiszár. We use these measures to compare sufficient experiments according to Blackwell's definition. In particular, we prove that if δ_X and δ_Y are two experiments and δ_X≥δ_Y then l_X≥l_y for all of the above measures.     

Simple Counterexamples against the Conditionality Principle

The famous theorem of Birnbaum, stating that the likelihood principle follows from the conditionality principle together with the sufficiency principle, has caused much discussion among statisticians. Briefly, many writers dislike the consequences of the likelihood principle (among other things, confidence coefficients and levels of tests are dismissed as meaningless), but at the same time they feel that both the conditionality principle and the sufficiency principle are intuitively obvious. In the present article we give examples to show that the conditionality principle should not be taken to be of universal validity, and we discuss some consequences of these examples.     

Some principles for surveillance adopted for multivariate processes with a common change point

The surveillance of multivariate processes has received growing attention during the last decade. Several generalizations of well-known methods such as Shewhart, CUSUM and EWMA charts have been proposed. Many of these multivariate procedures are based on a univariate summarized statistic of the multivariate observations, usually the likelihood ratio statistic. In this paper we consider the surveillance of multivariate observation processes for a shift between two fully specified alternatives. The effect of the dimension reduction using likelihood ratio statistics are discussed in the context of sufficiency properties. Also, an example of the loss of efficiency when not using the univariate sufficient statistic is given. Furthermore, a likelihood ratio method, the LR method, for constructing surveillance procedures is suggested for multivariate surveillance situations. It is shown to produce univariate surveillance procedures based on the sufficient likelihood ratios. As the LR procedure has several optimality properties in the univariate, it is also used here as a benchmark for comparisons between multivariate surveillance procedures     

ANCILLARIES SUFFICIENT FOR THE SAMPLE SIZE1

The process of recovery of information proceeds by conditioning on ancillaries which are part of the minimal sufficient statistic (MSS), so-called internal ancillaries. This is motivated by the notion that ancillaries function as precision indices. In some cases the ancillary can itself replace the sample size entirely in the model. It is decided that such ancillaries must be conditioned on if one of the sufficiency or conditionality principles is not to be violated.     

Building Community, Empowerment and Self-sufficiency

Started in January 2000, the Camfield Estates-MIT Creating Community Connections Project, a partnership between the Camfield Tenants Association (CTA) and the Massachusetts Institute of Technology (MIT), has the goal of establishing Camfield Estates as a model for other housing developments as to how individuals, families, and a community can make use of information and communications technology to support their interests and needs. To achieve this goal, CTA and MIT have formed a unique partnership and an infrastructure that combines the three primary models for community technology - a community network where state-of-the-art desktop computers, software, and high-speed Internet connectivity have been offered to every family, a community technology center (CTC) located on the premises in the community center, and community content delivered through a community-based web system, the Creating Community Connections (C3) System - along with an agenda to build community, empowerment, and self-sufficiency amongst the residents. This paper is a case study of the Camfield Estates-MIT project to date, including the history and background of the project, the approaches taken to build community, empowerment and self-sufficiency, the methodology that has been employed, and early results from the initiative.     

Some Elementary Theory of Permutation Tests

In literature, permutation tests are mostly derived by means of heuristic arguments (Edgington and Onghena, 2007 Edgington, E.S., Onghena, P. (2007). Randomization Tests (4th ed.). Boca Raton: Chapman & Hall/CRC.[Crossref] , [Google Scholar]; Good, 2005 Good, P. (2005). Permutation, Parametric, and Bootstrap Tests of Hypotheses (3rd ed.). New York: Springer-Verlag. [Google Scholar]). In this paper, we derive them within the sufficiency and conditionality principles of inference. Most important of their properties are exposed without formal proofs that can be found in the book by Pesarin and Salmaso (2010).     

Unbiased Estimation for the General Half-Normal Distribution

This work proposes unbiased estimators for the parameters of the general half-normal distribution that exhibit a better performance than the estimators based on maximum likelihood proposed in the literature. From these estimators, large sample confidence sets are also derived.     

From Optimality Robustness To Sufficiency and Completeness

Let (\Omega,{\cal A},{\cal P}) stand for a statistical experiment and {\cal B},{\cal C} for some sub- σ -algebras of {\cal A} with {\cal C}\subset {\cal B} . It is shown that for any {\cal B} -measurable d\in\bigcap_{P\in {\cal P}}\,{\cal L}_{2}(\Omega,{\cal A},P) there exists some d_{1}\in\bigcap_{P\in {\cal P}}{\cal L}_{2}(\Omega,{\cal A},P) being {\cal C} -measurable and a UMVU estimator in (\Omega,{\cal A},{\cal P}) and some conditional white noise d_{2}\in\bigcap_{P\in {\cal P}}\,{\cal L}_{2}(\Omega,{\cal A},P) , i.e. E_{P}(d_{2}\vert {\cal C})=0,P\in {\cal P} , satisfying d=d_{1}+d_{2} , where d_{j},j=1,2 , are uniquely determined up to P -zero sets, if and only if {\cal C} is sufficient and complete for {\cal P}\vert {\cal B} and {\cal B} is optimality robust for {\cal P} , i.e. any {\cal B} -measurable d\in\bigcap_{P\in {\cal P}}\,{\cal L}_{2}(\Omega,{\cal A},P) being some UMVU estimator in the restricted statistical experiment (\Omega,{\cal B},{\cal P}\vert {\cal B}) is already a UMVU estimator in the original statistical experiment (\Omega,{\cal A},{\cal P}) . In particular, the special case {\cal B}={\cal A} characterizes sufficiency and completeness of {\cal C} for {\cal P} and the special case {\cal B}={\cal C} optimality robustness and completeness of {\cal C} for {\cal P} from a decomposition theoretical point of view. As an application it is shown that a σ -algebra containing a sufficient and complete sub- σ -algebra is optimality robust without being itself in general neither sufficient nor complete.     

A precise estimator for the log-normal mean

The log-normal distribution is frequently encountered in applications. The uniformly minimum variance unbiased (UMVU) estimator for the log-normal mean is given explicitly by a formula found by Finney in 1941. In contrast to this the most commonly used estimator for a log-normal mean is the sample mean. This is possibly due to the complexity of the formula given by Finney. A modified maximum likelihood estimator which approximates the UMVU estimator is derived here. It is sufficiently simple to be implemented in elementary spreadsheet applications. An elementary approximate formula for the root-mean-square error of the suggested estimator and the UMVU estimator is presented. The suggested estimator is compared with the sample mean, the maximum likelihood, and the UMVU estimators by Monte Carlo simulation in terms of root-mean-square error.