青铜器断代与《周南·卷耳》篇——《诗经》起源时间新考

本文借鉴现代考古学的方法,以特定的青铜器器型作为定点标准,考证《诗经》中位列第三的《周南.卷耳》篇形成于殷末一周初时段。这一结果,与近年考古发现及传世文献的记录一道,形成坚实的证据网链,确证中国第一部诗歌总集--《诗经》的起源时间为殷末-周初时期。     

Ordinal ridge regression with categorical predictors

In multi-category response models, categories are often ordered. In the case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to the sample size. The likelihood estimates do not exist when the number of observations is less than the number of parameters. The same problem arises if constraint on the order of intercept values is not met during the iterative procedure. Proportional odds models (POMs) are most commonly used for ordinal responses. In this paper, penalized likelihood with quadratic penalty is used to address these issues with a special focus on POMs. To avoid large differences between two parameter values corresponding to the consecutive categories of an ordinal predictor, the differences between the parameters of two adjacent categories should be penalized. The considered penalized-likelihood function penalizes the parameter estimates or differences between the parameter estimates according to the type of predictors. Mean-squared error for parameter estimates, deviance of fitted probabilities and prediction error for ridge regression are compared with usual likelihood estimates in a simulation study and an application.     

Approximate penalization path for smoothly clipped absolute deviation

Feature selection often constitutes one of the central aspects of many scientific investigations. Among the methodologies for feature selection in penalized regression, the smoothly clipped and absolute deviation seems to be very useful because it satisfies the oracle property. However, its estimation algorithms such as the local quadratic approximation and the concave–convex procedure are not computationally efficient. In this paper, we propose an efficient penalization path algorithm. Through numerical examples on real and simulated data, we illustrate that our path algorithm can be useful for feature selection in regression problems.     

A generalized quantile regression model

A new class of probability distributions, the so-called connected double truncated gamma distribution, is introduced. We show that using this class as the error distribution of a linear model leads to a generalized quantile regression model that combines desirable properties of both least-squares and quantile regression methods: robustness to outliers and differentiable loss function.     

Computational approximation of the likelihood ratio for testing the existence of change-points in a heteroscedastic series

In this work, we present a computational method to approximate the occurrence of the change-points in a temporal series consisting of independent and normally distributed observations, with equal mean and two possible variance values. This type of temporal series occurs in the investigation of electric signals associated to rhythmic activity patterns of nerves and muscles of animals, in which the change-points represent the actual moments when the electrical activity passes from a phase of silence to one of activity, or vice versa. We confront the hypothesis that there is no change-point in the temporal series, against the alternative hypothesis that there exists at least one change-point, employing the corresponding likelihood ratio as the test statistic; a computational implementation of the technique of quadratic penalization is employed in order to approximate the quotient of the logarithmic likelihood associated to the set of hypotheses. When the null hypothesis is rejected, the method provides estimations of the localization of the change-points in the temporal series. Moreover, the method proposed in this work employs a posteriori processing in order to avoid the generation of relatively short periods of silence or activity. The method is applied to the determination of change-points in both experimental and synthetic data sets; in either case, the results of our computations are more than satisfactory.     

Adjusted power-divergence test statistics for simple goodness-of-fit under clustered sampling

Two-different types of adjustments to the power-divergence test statistics have been introduced for the problem of testing goodness-of-fit under clustered sampling. Penalization has also been introduced to handle the cells with zero frequencies. The asymptotic distribution of the proposed power-divergence test statistics has been investigated under clustered sampling and the performances of the proposed statistics for finite samples have been studied through a designed simulation study.     

法律职业主义视野下的香港律师惩戒制度

香港的律师制度渊源于英国,在律师团体管理方面表现出自主性、自律性的特点,具有典型的法律职业主义色彩。香港的律师惩戒制度作为体现上述特点的主要内容之一,具有民主性、程序性、公正性和司法性特征。具有法律职业主义色彩的律师伦理规范和律师惩戒制度,体现了社会一部分职业相对于国家所具有的独立性,但同时也是律师团体通过自我控制实现市场垄断所作出的努力。香港律师团体管理模式中所具有的这种两面性为大陆律师管理模式的改革和完善提供了很难得的研究样本。     

Robust confidence regions for multinomial probabilities

Generally, confidence regions for the probabilities of a multinomial population are constructed based on the Pearson χ² statistic. Morales et al. (Bootstrap confidence regions in multinomial sampling. Appl Math Comput. 2004;155:295–315) considered the bootstrap and asymptotic confidence regions based on a broader family of test statistics known as power-divergence test statistics. In this study, we extend their work and propose penalized power-divergence test statistics-based confidence regions. We only consider small sample sizes where asymptotic properties fail and alternative methods are needed. Both bootstrap and asymptotic confidence regions are constructed. We consider the percentile and the bias corrected and accelerated bootstrap confidence regions. The latter confidence region has not been studied previously for the power-divergence statistics much less for the penalized ones. Designed simulation studies are carried out to calculate average coverage probabilities. Mean absolute deviation between actual and nominal coverage probabilities is used to compare the proposed confidence regions.     

Paths Following Algorithm for Penalized Logistic Regression Using SCAD and MCP

In this article, we develop a generalized penalized linear unbiased selection (GPLUS) algorithm. The GPLUS is designed to compute the paths of penalized logistic regression based on the smoothly clipped absolute deviation (SCAD) and the minimax concave penalties (MCP). The main idea of the GPLUS is to compute possibly multiple local minimizers at individual penalty levels by continuously tracing the minimizers at different penalty levels. We demonstrate the feasibility of the proposed algorithm in logistic and linear regression. The simulation results favor the SCAD and MCP’s selection accuracy encompassing a suitable range of penalty levels.