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.     

Simulating the Evolution of the Implied Distribution

Motivated by the implied stochastic volatility literature (Britten–Jones and Neuberger, forthcoming; Derman and Kani, 1997; Ledoit and Santa–Clara, 1998) this paper proposes a new and general method for constructing smile–consistent stochastic volatility models. The method is developed by recognising that option pricing and hedging can be accomplished via the simulation of the implied risk neutral distribution. We devise an algorithm for the simulation of the implied distribution, when the first two moments change over time. The algorithm can be implemented easily, and it is based on an economic interpretation of the concept of mixture of distributions. It can also be generalised to cases where more complicated forms for the mixture are assumed.     

Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.     

A Note on Minimax Mixture of Distributions Free Procedure for Inventory Model with Variable Lead Time

In recent papers, Moon and Choi (1998) and Hariga and Ben-Daya (1999)considered a continuous review inventory model with a mixture of backordersand lost sales in which the lead time, the order quantity, and the reorder pointare decision variables was studied. Moreover, they also develop a minimaxdistribution free procedure for the problem. While the demands of differentcustomers are not identical in the lead time, then we can't only use a singledistribution (such as Moon & Choi (1998) and Hariga & Ben-Daya (1999))to describe the demand of the lead time. Hence, we correct and extend the modelof Moon and Choi (1998) and Hariga and Ben-Daya (1999) by considering thelead time demand with the mixture of distributions. In addition, we also applythe minimax mixture of distributions free approach to the model by simultaneouslyoptimizing the order quantity, the reorder point, and the lead time to devise a practical procedure which can be used without specific information on demand distribution.     

Sovereign Risk Contagion in East Asia: A Mixture of Time-Varying Copulas Approach

This study analyzes sovereign risk contagion between four East Asian economies (China, Hong Kong, Japan, and Korea) and its structural changes through the Global Financial Crisis (GFC) and the European Debt Crisis (EDC) by applying the mixture of time-varying copulas to those economies’ credit default swap (CDS) spreads.

This article first finds a strong contagion from the US and PIIGS economies to the East Asian sovereign CDS markets and intraregional contagion within the East Asian markets. Second, the impact of contagion is different according to whether it is measured by the linear (Gaussian) or the upper tail dependence. Third, Japan plays an important role in increasing the linear dependence whereas China and Korea are crucial in terms of the upper tail dependence. Lastly, the GFC has structurally increased the linear dependence but not the upper tail dependence between the East Asian sovereign CDS markets.     

Cox Point Processes: Why One Realisation Is Not Enough

We review a rich class of point process models, Cox point processes, and illustrate the necessity of more than one observation (point patterns) in performing parameter estimation. Furthermore, we introduce a new Cox point process model by treating the intensity function of the underlying Poisson point process as a random mixture of normal components. The behaviour and performance of the new model are compared with those of popular Cox point process models. The new model is exemplified with an application that involves a single point pattern corresponding to earthquake events in California, USA.     

Interpoint Distance Test of Homogeneity for Multivariate Mixture Models

Finite mixtures offer a rich class of distributions for modelling of a variety of random phenomena in numerous fields of study. Using the sample interpoint distances (IPDs), we propose the IPD‐test statistic for testing the hypothesis of homogeneity of mixture of multivariate power series distribution or multivariate normal distribution. We derive the distribution of the IPDs that are drawn from a finite mixture of the multivariate power series distribution and multivariate normal distribution. Based on the empirical distribution of the IPDs, we construct a bootstrap test of homogeneity for other multivariate finite mixture models. The IPD test is applied to mixture models for matrix‐valued distributions and a test of homogeneity for Wishart mixture is presented. Numerical comparisons show that IPD test has accurate type I errors and is more powerful in most multivariate cases than the expectation–maximization (EM) test and modified likelihood ratio test.     

A number‐of‐modes reference rule for density estimation under multimodality

We consider kernel density estimation for univariate distributions. The question of interest is as follows: given that the data analyst has some background knowledge on the modality of the data (for instance, ‘data of this type are usually bimodal’), what is the adequate bandwidth to choose? We answer this question by extending Silverman's idea of ‘normal‐reference’ to that of ‘reference to a Gaussian mixture’. The concept is illustrated in the light of real data examples.     

低温条件下泵站建设中大体积混凝土裂缝控制研究

文章结合西安市尚苑路雨水泵站工程中的大体积混凝土的设计与施工,对低温条件下进行施工的大体积混凝土的裂缝控制技术进行研究。     

降雨作用下土石混合体边坡力学参数弱化规律与稳定性分析

土石混合体边坡地质灾害是中国最为常见的地质灾害之一,而降雨是其主要诱发因素,降雨条件下边坡劣化机理研究是防灾减灾领域的重要研究方向.通过分析不同含水率条件下土石混合体力学性质试验数据,建立抗剪强度参数随含水率的演化方程;以此为基础,针对西南地区典型土石混合体边坡,考虑降雨条件下力学参数弱化的影响,采用Geostudio软件开展边坡稳定性分析.结果表明:演化方程能有效反映含水率增大对土石混合体抗剪强度具有显著的弱化效应;降雨强度的增大会显著降低边坡稳定性,降雨持时只是降雨过程中某一时刻值的体现,对边坡稳定性影响不大;降雨量与边坡安全系数呈明显线性负相关.研究成果可为土石混合体边坡地质灾害预测与防治提供技术支撑.