Confession Governance and Verdicts in China’s Criminal Justice

The objective phenomenon of reliance on confessions is the source of many problems in the practice of criminal justice in China. Although successive amendments to the law have endeavored to improve the handling of confessions, they have been unable to resolve the dilemma of a rigid management mode, incompatible management strategies, and conflicting attitudes towards management. This paper has made a multi-dimensional reinterpretation of it from the judges’ perspective, including functional analysis, cultural interpretation and immediate considerations. It reveals that behind its functions of offering moral comfort or allowing evasion of responsibility, confession, as a medium of physical and intellectual management, has the more important function of supplementing and improving the legitimacy of criminal verdicts. The remolding of the legitimacy of criminal verdicts with the coordinated improvement of the management of confessions will change the mode of association between those who currently govern confession and those they govern and will clash with the current closed mode of criminal justice management, thereby promoting a transformation in the governance of criminal justice.     

Infill asymptotics for adaptive kernel estimators of spatial intensity

We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.     

Books on probability ideas are reviewed

In this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645–660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985–991; F.A. Ziegelmann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543–1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index and (adaptive) functional-coefficient, in volatility estimation, all motivated as alternatives to deal with the curse of dimensionality present in fully nonparametric models; and (2) propose the use of a variation of the traditional cross-validation method to deal with model choice in the class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided.     

Quantile regression and variable selection for the single-index model

In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties of the proposed estimator are derived. Furthermore, we propose a variable selection procedure for the QR of SIM by combining the estimation method with the adaptive LASSO penalized method to get sparse estimation of the index parameter. The oracle properties of the variable selection method are established. Simulations with various non-normal errors are conducted to demonstrate the finite sample performance of the estimation method and the variable selection procedure. Furthermore, we illustrate the proposed method by analyzing a real data set.     

Into the IRIS: a model for analyzing identity dynamics in conflict

I propose a lens model for understanding how core identities (ethnic, sectarian, racial or national) are affected by past interactions and narratives increasing the chances of the outbreak of violent conflict. The model can be used to show how core identities are polarized during conflict escalation and how they might de-polarize during periods of de-escalation and conflict resolution, leading to potential transformation of core identities in post-conflict situations. Northern Ireland will be used to illustrate the Integrated Relational Identity Structure model, showing how conflicting identities have changed, or not, as a result of history, the conflict and the peace process.     

The Box–Cox Transformation and Non‐Iterative Estimation Methods for Ordinal Log‐Linear Models

Recently Beh and Farver investigated and evaluated three non‐iterative procedures for estimating the linear‐by‐linear parameter of an ordinal log‐linear model. The study demonstrated that these non‐iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box–Cox transformation. We also show that by using this transformation, accurate non‐iterative estimates are achievable even when a contingency table contains sampling zeros.     

From advocacy to service provision: state transformation and the disability rights movement in Taiwan

This research uses the development of the disability rights movement in Taiwan as a case study to analyze the impact of state transformation, in particular marketization of social welfare policy, on the disability rights movement. First, the institutionalization of the disability rights movement enabled it to expand its organizational structure and become involved in shaping policy. Secondly, when disability rights organizations started to undertake state-funded projects, their focus shifted from advocacy to service provision. Thirdly, competition for limited state-funding gave the organizations led by urban-middle class advocates a significant advantage over small, community-based NPOs and gathered significantly greater resources. Finally, this paper suggests that, in a context in which the state did not provide basic social services for its citizens with disabilities, the institutionalization of SMOs turned advocacy groups into service providers. Although the number of disability civic organizations increased, the voices of advocacy groups were weakened.     

菲利普斯曲线在中国的有效性研究

从基本的菲利普斯曲线理论出发,提出了新的适应性预期模型,并以此为基础建立附加预期的菲利普斯曲线方程,进而估计出我国近年来的自然失业率水平,由此证明了菲利普斯曲线在我国的有效性,并描述了预期因素对实际通货膨胀水平的影响。最后结合实证分析结果提出解决我国通货膨胀和失业问题的建议,如保证货币政策的连贯性、加强信息披露、解决结构性失业问题等。     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.