A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments

The reproducibility crisis, that is, the fact that many scientific results are difficult to replicate, pointing to their unreliability or falsehood, is a hot topic in the recent scientific literature, and statistical methodologies, testing procedures and p‐values, in particular, are at the centre of the debate. Assessment of the extent of the problem–the reproducibility rate or the false discovery rate–and the role of contributing factors are still an open problem. Replication experiments, that is, systematic replications of existing results, may offer relevant information on these issues. We propose a statistical model to deal with such information, in particular to estimate the reproducibility rate and the effect of some study characteristics on its reliability. We analyse data from a recent replication experiment in psychology finding a reproducibility rate broadly coherent with other assessments from the same experiment. Our results also confirm the expected role of some contributing factor (unexpectedness of the result and room for bias) while they suggest that the similarity between original study and the replica is not so relevant, thus mitigating some criticism directed to replication experiments.     

Model selection in random effects models for directed graphs using approximated Bayes factors

With the development of an MCMC algorithm, Bayesian model selection for the p ₂ model for directed graphs has become possible. This paper presents an empirical exploration in using approximate Bayes factors for model selection. For a social network of Dutch secondary school pupils from different ethnic backgrounds it is investigated whether pupils report that they receive more emotional support from within their own ethnic group. Approximated Bayes factors seem to work, but considerable margins of error have to be reckoned with.     

Nearly exact sample size calculation for powerful non‐randomized tests for differences between binomial proportions

In the case of two independent samples, it turns out that among the procedures taken in consideration, BOSCHLOO'S technique of raising the nominal level in the standard conditional test as far as admissible performs best in terms of power against almost all alternatives. The computational burden entailed in exact sample size calculation is comparatively modest for both the uniformly most powerful unbiased randomized and the conservative non‐randomized version of the exact Fisher‐type test. Computing these values yields a pair of bounds enclosing the exact sample size required for the Boschloo test, and it seems reasonable to replace the exact value with the middle of the corresponding interval. Comparisons between these mid‐N estimates and the fully exact sample sizes lead to the conclusion that the extra computational effort required for obtaining the latter is mostly dispensable. This holds also true in the case of paired binary data (McNemar setting). In the latter, the level‐corrected score test turns out to be almost as powerful as the randomized uniformly most powerful unbiased test and should be preferred to the McNemar–Boschloo test. The mid‐N rule provides a fairly tight upper bound to the exact sample size for the score test for paired proportions.     

企业改制中税收流失问题的分析

在改制过程中,有些企业为了一己私欲,采用转移税源、模糊产权、钻体制漏洞等手段,偷税、漏税,导致大量税收流失,使国家遭受巨大损失。应统一对改制企业欠税问题的认识,加强对各种所有制性质企业的税收管理,防范和制止企业借改制、改组之机逃避税收债务;完善税收征管法,以确保国家税收债权,维护国家合法权益。     

基于价值链的人力资源4p管理模式研究

人力资源价值链管理是一种以价值为中心的基于协作的策略。它能够有效地提高企业的竞争力,赢得竞争优势并实现人力资本的价值增值。通过对人力资源价值链当中的价值活动的分析,构建以价值为导向,以价值分析为切入点,来实现价值培养、价值评价、价值分配的有机结合和良性循环的人力资源4P管理模式。     

Stable Asymptotics for M‐estimators

We review some first‐order and higher‐order asymptotic techniques for M‐estimators, and we study their stability in the presence of data contaminations. We show that the estimating function (ψ) and its derivative with respect to the parameter ps://wol-prod-cdn.literatumonline.com/cms/attachment/0a30a8a4-fda0-4a81-8660-44ace8f6d906/insr12102-math-0001.png"> play a central role. We discuss in detail the first‐order Gaussian density approximation, saddlepoint density approximation, saddlepoint test, tail area approximation via the Lugannani–Rice formula and empirical saddlepoint density approximation (a technique related to the empirical likelihood method). For all these asymptotics, we show that a bounded ψ (in the Euclidean norm) and a bounded ps://wol-prod-cdn.literatumonline.com/cms/attachment/c3952c09-d213-46b7-a196-2e56c24bd3d4/insr12102-math-0002.png"> (e.g. in the Frobenius norm) yield stable inference in the presence of data contamination. We motivate and illustrate our findings by theoretical and numerical examples about the benchmark case of one‐dimensional location model.     

我国农村居民消费水平预测的实证研究

本文介绍了自回归移动平均模型即ARIMA(p,d,q)模型的基本原理、构建及其应用方法。文章广泛搜集从1952年到2011年以来我国农村居民消费水平的相关数据,运用统计学和计量经济学原理,从时间序列的定义出发,结合统计软件E-views运用ARMA建模方法,将ARIMA模型应用于我国农村居民消费水平的分析与预测,得到较为满意的结果。     

Coherent forecasting for count time series using Box–Jenkins's AR(p) model

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.     

Unconditional tests for association in 2 × 2 contingency tables in the total sum fixed design

The asymptotic approach and Fisher's exact approach have often been used for testing the association between two dichotomous variables. The asymptotic approach may be appropriate to use in large samples but is often criticized for being associated with unacceptable high actual type I error rates for small to medium sample sizes. Fisher's exact approach suffers from conservative type I error rates and low power. For these reasons, a number of exact unconditional approaches have been proposed, which have been seen to be generally more powerful than exact conditional counterparts. We consider the traditional unconditional approach based on maximization and compare it to our presented approach, which is based on estimation and maximization. We extend the unconditional approach based on estimation and maximization to designs with the total sum fixed. The procedures based on the Pearson chi‐square, Yates's corrected, and likelihood ratio test statistics are evaluated with regard to actual type I error rates and powers. A real example is used to illustrate the various testing procedures. The unconditional approach based on estimation and maximization performs well, having an actual level much closer to the nominal level. The Pearson chi‐square and likelihood ratio test statistics work well with this efficient unconditional approach. This approach is generally more powerful than the other p‐value calculation methods in the scenarios considered.