A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

Bootstrap methods for bias correction and confidence interval estimation for nonlinear quantile regression of longitudinal data

This paper examines the use of bootstrapping for bias correction and calculation of confidence intervals (CIs) for a weighted nonlinear quantile regression estimator adjusted to the case of longitudinal data. Different weights and types of CIs are used and compared by computer simulation using a logistic growth function and error terms following an AR(1) model. The results indicate that bias correction reduces the bias of a point estimator but fails for CI calculations. A bootstrap percentile method and a normal approximation method perform well for two weights when used without bias correction. Taking both coverage and lengths of CIs into consideration, a non-bias-corrected percentile method with an unweighted estimator performs best.     

The boomerang effect of psychological interventions

Research has found that teaching people about psychological biases can help counteract biased behavior. On the other hand, due to the innate need for preservation of a positive self-image, it is likely that teaching people about biases they hold, may cause a boomerang effect in cases where being associated with a specific bias implies negative social connotations. In the three studies below we examine situations in which psychological bias implies negatively associated behavior, and show that teaching people about bias in those contexts can be counterproductive.     

Spatial Distribution of Flood Risk and Quality of Spatial Management: Case Study in Odra Valley,Poland

This article presents methodological solutions aimed at presenting the spatial distribution of flood risk and quality of spatial management (land use), indicating both those areas used reasonably and those requiring modification. The purpose was to identify key risk areas and risk‐free areas from the point of view of human security and activity on the floodplains, based on the examples of the vicinities of Wroclaw and Raciborz in the Odra Valley, Poland. Due to recent climate change, Poland has suffered the effects of severe flooding (e.g., 1997, 2001, 2010). The analyses conducted were motivated by the European Parliament and Council's recently implemented Directive 2007/60/WE, as well as by the demand for studies for local spatial planning. The analysis indicates that reasonably developed areas do not account for the majority of those studied, making up 36% of the Wroclaw area and 15% of the Raciborz area.     

Numerical evaluation of tests based on different heteroskedasticity-consistent covariance matrix estimators

This article considers the issue of performing tests in linear heteroskedastic models when the test statistic employs a consistent variance estimator. Several different estimators are considered, namely: HC0, HC1, HC2, HC3, and their bias-adjusted versions. The numerical evaluation is performed using numerical integration methods; the Imhof algorithm is used to that end. The results show that bias-adjustment of variance estimators used to construct test statistics delivers more reliable tests when they are performed for the HC0 and HC1 estimators, but the same does not hold for the HC3 estimator. Overall, the most reliable test is the HC3-based one.     

Pricing arithmetic Asian option under a two-factor stochastic volatility model with jumps

This paper presents an efficient Monte Carlo simulation scheme based on the variance reduction methods to evaluate arithmetic average Asian options in the context of the double Heston's stochastic volatility model with jumps. This paper consists of two essential parts. The first part presents a new flexible stochastic volatility model, namely, the double Heston model with jumps. In the second part, by combining two variance reduction procedures via Monte Carlo simulation, we propose an efficient Monte Carlo simulation scheme for pricing arithmetic average Asian options under the double Heston model with jumps. Numerical results illustrate the efficiency of our method.     

多元的群体 偏离的印象——对中国“90后”社会特征的解析

"90后"群体是当前社会关注的焦点。以中国社会发展进程为纵线,以影响"90后"群体的主要心理因素为横线,从社会化进程、教育化选择、网络化生活以及同一性承诺等四个方面对"90后"群体的多元可能和形成过程进行解析发现:"90后"的天生多元促成了其群体多元的真正实现:"90后"教育选择的多样性加剧了其多元的可能性;"90后"的网络化生存(工作)能力提供了其多元的现实工具;"90后"的自我意识中心反映了其较早的自我觉醒。因此,面对社会给予"90后"的诸多负面评价与"90后"良好自我认同的差异与偏离,我们需要聆听他们对世界的认知。     

Asset Accumulation: An Alternative Approach to Achieving the Millennium Development Goals

The United Nations Millennium Development Goals (MDGs) are unlikely to be achieved by 2015, owing to conceptual flaws in their design as well as the structural and political constraints faced during implementation at the country level. While criticism of the MDGs is widespread, innovative ideas on addressing these operational challenges are still scanty. By reviewing a number of experiences, including those of the Foundation for the Promotion of Local Development (PRODEL) in Nicaragua and the Ministry of Cities in Brazil, this article highlights the importance of incorporating an asset‐accumulation perspective into MDG‐related policies and programmes as a way of generating an enabling environment that opens up new opportunities for poverty reduction in the cities of low‐ and middle‐low‐income countries.     

Application of the Bootstrap Approach to the Choice of Dimension and the α Parameter in the SIRα Method

To reduce the dimensionality of regression problems, sliced inverse regression approaches make it possible to determine linear combinations of a set of explanatory variables X related to the response variable Y in general semiparametric regression context. From a practical point of view, the determination of a suitable dimension (number of the linear combination of X) is important. In the literature, statistical tests based on the nullity of some eigenvalues have been proposed. Another approach is to consider the quality of the estimation of the effective dimension reduction (EDR) space. The square trace correlation between the true EDR space and its estimate can be used as goodness of estimation. In this article, we focus on the SIR_α method and propose a naïve bootstrap estimation of the square trace correlation criterion. Moreover, this criterion could also select the α parameter in the SIR_α method. We indicate how it can be used in practice. A simulation study is performed to illustrate the behavior of this approach.     

Composite quantile regression for varying-coefficient single-index models

ABSTRACT

The varying-coefficient single-index model (VCSIM) is a very general and flexible tool for exploring the relationship between a response variable and a set of predictors. Popular special cases include single-index models and varying-coefficient models. In order to estimate the index-coefficient and the non parametric varying-coefficients in the VCSIM, we propose a two-stage composite quantile regression estimation procedure, which integrates the local linear smoothing method and the information of quantile regressions at a number of conditional quantiles of the response variable. We establish the asymptotic properties of the proposed estimators for the index-coefficient and varying-coefficients when the error is heterogeneous. When compared with the existing mean-regression-based estimation method, our simulation results indicate that our proposed method has comparable performance for normal error and is more robust for error with outliers or heavy tail. We illustrate our methodologies with a real example.