学业质量增值评价数学模型的比较研究——以西部某区教育实证数据为例

增值评价是以学生成长为核心,以学校发展为目标的一种评价方式,可以更公平地评价学校效能。增值评价有多种数学模型,其中,与基于平均名次、基于标准分的两种模型相比,基于学生成长百分位和多层线性回归两种模型的优势在于能够避免优秀学生增值空间受限的问题。在多层线性回归模型中纳入学生性别、家庭社会经济地位指数等学校不可控的生源因素,可有效排除生源因素对学生的影响,更符合关注学校“净效能”的评价理念。     

Statistical inference on asymptotic properties of two estimators for the partially linear single-index models

The outer product of gradients (OPG) estimation procedure based on least squares (LS) approach has been presented by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] to estimate the single-index parameter in partially linear single-index models (PLSIM). However, its asymptotic property has not been established yet and the efficiency of LS-based method can be significantly affected by outliers and heavy-tailed distributions. In this paper, we firstly derive the asymptotic property of OPG estimator developed by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] in theory, and a novel robust estimation procedure combining the ideas of OPG and local rank (LR) inference is further developed for PLSIM along with its theoretical property. Then, we theoretically derive the asymptotic relative efficiency (ARE) of the proposed LR-based procedure with respect to LS-based method, which is shown to possess an expression that is closely related to that of the signed-rank Wilcoxon test in comparison with the t-test. Moreover, we demonstrate that the new proposed estimator has a great efficiency gain across a wide spectrum of non-normal error distributions and almost not lose any efficiency for the normal error. Even in the worst case scenarios, the ARE owns a lower bound equalling to 0.864 for estimating the single-index parameter and a lower bound being 0.8896 for estimating the nonparametric function respectively, versus the LS-based estimators. Finally, some Monte Carlo simulations and a real data analysis are conducted to illustrate the finite sample performance of the estimators.     

Asymptotic properties of a rank estimate in linear regression with symmetric non-identically distributed errors

In this article, a simple linear regression model with independent and symmetric but non-identically distributed errors is considered. Asymptotic properties of the rank regression estimate defined in Jaeckel [Estimating regression coefficients by minimizing the dispersion of the residuals, Ann. Math. Statist. 43 (1972), pp. 1449–1458] are studied. We show that the studied estimator is consistent and asymptotically normally distributed. The cases of bounded and unbounded score functions are examined separately. The regularity conditions of the article are exemplified for finite mixture distributions.     

Tail-Adaptive Location Rank Test for the Generalized Secant Hyperbolic Distribution

The generalized secant hyperbolic distribution (GSHD) was recently introduced as a modeling tool in data analysis. The GSHD is a unimodal distribution that is completely specified by location, scale, and shape parameters. It has also been shown elsewhere that the rank procedures of location are regular, robust, and asymptotically fully efficient. In this article, we study certain tail weight measures for the GSHD and introduce a tail-adaptive rank procedure of location based on those tail weight measures. We investigate the properties of the new adaptive rank procedure and compare it to some conventional estimators.     

How dominance hierarchies emerge from conflict: A game theoretic model and experimental evidence

We develop a game theoretic model of conflict and empirically test its predictions to study the emergence of social hierarchies in small groups. Previous research shows uncertainty about actors' ability may lead to more conflict; conflict demonstrates actors' ability and establishes relationships of dominance and submissiveness. Since we assume uncertainty regarding ability to be a crucial cause of conflict, we focus on the effects of different information conditions. We posit that actors know the distribution of abilities in their group and vary whether or not they know (1) their own ability and (2) their interaction partners' interaction histories. Our results from a laboratory experiment closely match qualitative model predictions. Most importantly, conflict produces information about actors’ ability, which reduces subsequent conflict. In an exploratory analysis we investigate to what extent gender, social value orientation, risk preferences and a competitive personality account for the quantitative discrepancies between model predictions and subject behavior.     

政治仪式中的权力结构及其动态分析

本文从势能的角度提出一种理解政治权力的理论图式,权力的涵括范围和价值位阶是衡量权力关系的两大基本变量。在政治仪式中,权力的涵括范围主要指仪式的类型、政治背景和政治空间、参与者以及规模等方面的差别;权力的价值位阶则主要取决于仪式主导者所处地位的绝对高度。这两种变量不仅是政治仪式中评估权力关系的关键指标,同时在实践中,政治仪式通过调控两种变量实现权力的生产和再生产。     

Nonparametric monitoring of equal predictive ability

This paper introduces a novel sequential approach for online surveillance of the equal predictive ability (EPA) hypothesis presumed to hold for many competing forecasting models. A nonparametric control chart is suggested for providing a decision at every new time point as to whether the EPA hypothesis remains valid. The detection ability of our procedure is evaluated in a Monte Carlo simulation study for various types of deviations from the EPA hypothesis. Our approach enables the quick detection of various shift types, is parsimonious, and robust to misspecifications. Based on these results, we formulate practical recommendations for procedure design.     

The Copula Information Criteria

We derive two types of Akaike information criterion (AIC)‐like model‐selection formulae for the semiparametric pseudo‐maximum likelihood procedure. We first adapt the arguments leading to the original AIC formula, related to empirical estimation of a certain Kullback–Leibler information distance. This gives a significantly different formula compared with the AIC, which we name the copula information criterion. However, we show that such a model‐selection procedure cannot exist for copula models with densities that grow very fast near the edge of the unit cube. This problem affects most popular copula models. We then derive what we call the cross‐validation copula information criterion, which exists under weak conditions and is a first‐order approximation to exact cross validation. This formula is very similar to the standard AIC formula but has slightly different motivation. A brief illustration with real data is given.