Nonadaptive group testing with lies: Probabilistic existence theorems

We consider a wide range of combinatorial group testing problems with lies including binary, additive and multiaccess channel group testing problems. We derive upper bounds for the number of tests in the optimal nonadaptive algorithms. The derivation is probabilistic and is therefore non-constructive; it does not provide the way of constructing optimal algorithms. In the asymptotic setting, we show that the leading term for the number of tests does not depend on the number of lies and it is thus the same as for the zero-lie case. However, the other terms in the asymptotic upper bounds depend on the number of lies and substantially influence the upper bounds in the non-asymptotic situation.     

A NEW FAMILY OF DIVERGENCE MEASURES FOR TESTS OF FIT

The aim of this work is to investigate a new family of divergence measures based on the recently introduced Basu, Harris, Hjort and Jones (BHHJ) measure of divergence (Biometrika 85 , 549–559). The new family is investigated in connection with hypothesis testing problems, and new test statistics are proposed. Simulations are performed to check the appropriateness of the proposed test statistics.     

On Efficient Estimators of the Proportion of True Null Hypotheses in a Multiple Testing Setup

We consider the problem of estimating the proportion θ of true null hypotheses in a multiple testing context. The setup is classically modelled through a semiparametric mixture with two components: a uniform distribution on interval [0,1] with prior probability θ and a non‐parametric density f . We discuss asymptotic efficiency results and establish that two different cases occur whether f vanishes on a non‐empty interval or not. In the first case, we exhibit estimators converging at a parametric rate, compute the optimal asymptotic variance and conjecture that no estimator is asymptotically efficient (i.e. attains the optimal asymptotic variance). In the second case, we prove that the quadratic risk of any estimator does not converge at a parametric rate. We illustrate those results on simulated data.     

Conditional Bayesian task of testing many hypotheses

Conditional Bayesian task of testing many hypotheses is stated and solved. The concept of conditionality is used for the designation of the fact that the Bayesian task is stated as a conditional optimization problem where the probability of one-type error is restricted and, under such a condition, the probability of second-type error is minimized. The offered statement gives the decision rule which allows us not to accept any hypothesis if, on the basis of the available information, it is impossible to make a decision with the set significance level. In such a case, it is necessary to ensure the additional information in the form of additional observation results or a change in the significant level of hypotheses testing. These properties make our statement more general than the usual statement of the Bayesian problem which is a special case of the one offered and improve the reliability of the made decision. The calculation results completely confirm the results of theoretical investigations.     

Conditional Stochastic Dominance Testing

This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional distributions and their moments. We exploit the fact that a conditional distribution dominates the other if and only if the difference between the marginal joint distributions is monotonic in the explanatory variable at each value of the dependent variable. The proposed test statistic compares restricted and unrestricted estimators of the difference between the joint distributions, and it can be implemented under minimal smoothness requirements on the underlying nonparametric curves and without resorting to smooth estimation. The finite sample properties of the proposed test are examined by means of a Monte Carlo study. We illustrate the test by studying the impact on postintervention earnings of the National Supported Work Demonstration, a randomized labor training program carried out in the 1970s.     

浅议立案程序及其改革

本文从立案审查行为的性质及立案的阶段性目标的设置入手 ,论证了立案不应成为诉讼案件的一个独立阶段。认为在刑事审判前程序一体化的大格局下 ,设立新的刑事诉讼启动程序 ,并使其更符合诉讼规律和价值的要求     

认识、游戏与想象——对文学功利性问题的思考

萨特的存在主义文学观念,把文学理解为一种在非现实的水平上向他人揭示世界的活动,因此,在承认文学艺术与社会生活的密切相关性的同时,说明了它相对的独立性和自律性,确定了它与其他人类行为的区别性特征.在此基础上,对文艺活动的社会角色的认定也就与西方文学理论传统区别开了,在后者那里,文学艺术主要在某种边缘性的、次生的意义上得到规定,即它总是作为科学认识行为和政治社会实践(这两种人的生存实践中更加直观的力量)的附庸被人们把握,它是某种不完善的知识或者只是游戏.     

Conditional inference under simultaneous stochastic ordering constraints

Testing for stochastic ordering is of considerable importance when increasing does of a treatment are being compared, but in applications involving multivariate responses has received much less attention. We propose a permutation test for testing against multivariate stochastic ordering. This test is distribution-free and no assumption is made about the dependence relations among variables. A comparative simulation study shows that the proposed solution exhibits a good overall performance when compared with existing tests that can be used for the same problem.     

自实时读写的E~2PROM系统设计

本文给出了一个能实现对E~2PROM自实时读写的专用智能系统,它将E~2PROM空间作为数据存贮区,从而使系统能在断电状态保持实时参数或者初始化结果。 面向实际应用,这一工作的意义十分明显。