Bayesian monitoring of local residual autocorrelations taking into account the run-length

This paper proposes new two-sided monitoring algorithms for detecting the presence of first order residual autocorrelations in Dynamic Normal Models. The methodology uses a Bayesian decision approach with loss function which takes into account the run-length of the process. The power and mean run-length of the proposed algorithms are analysed by Monte Carlo methods. The results obtained improve those corresponding to the monitoring algorithm for residual autocorrelations proposed in Gargallo and Salvador [2003. Monitoring residual autocorrelations in dynamic linear models. Comm. Statist. Simulation Comput. 32(4), 1079–1104.] with respect to the run-length, and also exhibit more homogeneous behaviour.     

A group sequential test for the inverse Gaussian mean

The present paper deals with the development of a group sequential test when response variable has an inverse Gaussian distribution with known scale parameter.     

Adaptive interval estimation in one-way random effects models

Two-stage procedures are introduced to control the width and coverage (validity) of confidence intervals for the estimation of the mean, the between groups variance component and certain ratios of the variance components in one-way random effects models. The procedures use the pilot sample data to estimate an “optimal” group size and then proceed to determine the number of groups by a stopping rule. Such sampling plans give rise to unbalanced data, which are consequently analyzed by the harmonic mean method. Several asymptotic results concerning the proposed procedures are given along with simulation results to assess their performance in moderate sample size situations. The proposed procedures were found to effectively control the width and probability of coverage of the resulting confidence intervals in all cases and were also found to be robust in the presence of missing observations. From a practical point of view, the procedures are illustrated using a real data set and it is shown that the resulting unbalanced designs tend to require smaller sample sizes than is needed in a corresponding balanced design where the group size is arbitrarily pre-specified.     

On Bayesian estimators in multistage binomial designs

A new class of Bayesian estimators for a proportion in multistage binomial designs is considered. Priors belong to the beta-J distribution family, which is derived from the Fisher information associated with the design. The transposition of the beta parameters of the Haldane and the uniform priors in fixed binomial experiments into the beta-J distribution yields bias-corrected versions of these priors in multistage designs. We show that the estimator of the posterior mean based on the corrected Haldane prior and the estimator of the posterior mode based on the corrected uniform prior have good frequentist properties. An easy-to-use approximation of the estimator of the posterior mode is provided. The new Bayesian estimators are compared to Whitehead's and the uniformly minimum variance estimators through several multistage designs. Last, the bias of the estimator of the posterior mode is derived for a particular case.     

Chain pooling model selection for two-level fixed effects factorial experiments

As many as three iterated statistical model deletion procedures are considered for an experiment.Population model coeff cients were chosen to simulate a saturated 2⁴experiment having an unfavorable distribution of parameter values.Using random number studies, three model selection strategies were developed, namely, (1) a strategy to be used in anticipation of large coefficients of variation (neighborhood of 65 percent), (2) strategy to be used in anticipation of small coefficients of variation (4 percent or less), and (3) a security regret strategy to be used in the absence of such prior knowledge     

Bounding the resampling risk for sequential Monte Carlo implementation of hypothesis tests

Sequential designs can be used to save computation time in implementing Monte Carlo hypothesis tests. The motivation is to stop resampling if the early resamples provide enough information on the significance of the p-value of the original Monte Carlo test. In this paper, we consider a sequential design called the B-value design proposed by Lan and Wittes and construct the sequential design bounding the resampling risk, the probability that the accept/reject decision is different from the decision from complete enumeration. For the B-value design whose exact implementation can be done by using the algorithm proposed in Fay, Kim and Hachey, we first compare the expected resample size for different designs with comparable resampling risk. We show that the B-value design has considerable savings in expected resample size compared to a fixed resample or simple curtailed design, and comparable expected resample size to the iterative push out design of Fay and Follmann. The B-value design is more practical than the iterative push out design in that it is tractable even for small values of resampling risk, which was a challenge with the iterative push out design. We also propose an approximate B-value design that can be constructed without using a specially developed software and provides analytic insights on the choice of parameter values in constructing the exact B-value design.     

Sequential Hypothesis Tests for Identifying the Minimum Dose with a Threshold Effect

We propose a procedure to identify a lowest dose having greater effect than a threshold dose under the assumption of monotonicity of dose mean response in dose response test. So, we use statistics based on contrasts among sample means and apply a group sequential procedure to our procedure to identify effectively the dose. If we can identify the dose at an early step in the sequential test, since we can terminate the procedure with a few observations, the procedure is useful from an economical point of view. In a simulation studies, we compare the superiority among these procedures based on three contrasts.     

和谐社会构建中未成年人犯罪预防机制探析

现阶段．我国未成年人犯罪预防机制存在矫正措施单一、监禁刑的适用比例过高、立法粗疏而不细致的缺失。形成建立在青少年身心特征基础上的完整的法律体系、扩大非监禁刑的适用比例、设立未成年人犯罪前科消灭制度、建立未成年人司法、执法一体化制度势在必行。     

我国未来民法典中私法责任承担方式的立法选择——兼论停止侵害、排除妨碍、消除危险、返还财产的二元定位

我国《民法通则》、《物权法》和《侵权责任法》同时规定了停止侵害、排除妨碍、消除危险、返还财产等救济方式.在以上三部法律中,这些救济方式性质、内涵是否相同并不明确,在法律适用过程中极易引起混乱,也为未来民法典设计私法责任承担方式设下了制度障碍.本文认为,停止侵害、排除妨碍、消除危险、返还财产既可以作为绝对权请求权,又可以作为私法责任承担方式,二者具有各自的独立性.这种二元定位具有理论与现实的意义,未来民法典应该作出科学的立法技术处理.     

国际扫盲趋势及其内涵的新发展

本文根据国际扫盲教育的历史演进及其内涵的发展，把扫盲分为基础性扫盲、功能性扫盲和文化性扫盲三种，在总结和分析扫盲三个阶段的共同特点及存在问题的基础上，提出了“发展性扫盲”的概念，对当前国际扫盲及发展趋势进行了概括，从扫盲目的和评价标准、扫盲主体和对象、扫盲内容和形式三个方面对其内涵进行说明，提出了发展性扫盲的四个特点：基础性、动态性、终身性和全民性。