罗尔斯政治哲学中的平等观探究

罗尔斯是20世纪探索平等问题最伟大的政治哲学家、伦理学家之一。罗氏穷其一生探索平等的理论,论证“作为公平正义”的体系。正义原则是他平等思想体系的核心,也是他用以指导社会基本结构的理论模型。正义原则分为两部分并概括为三个原则,每条原则都蕴含着罗氏对特定领域平等问题的关切,原则之间的实现顺序表达了他对实现平等步骤的思考。平等是一个具有多重属性、多种含义的复杂概念,罗氏运用独特的理论方法对平等的含义及其实现顺序进行独具匠心的诠释。罗氏的平等观不但为政治哲学的思想大厦增添新的内容,而且为当代中国的政治实践提供理论启迪。     

A Queueing Theoretic Approach to Set Staffing Levels in Time‐Dependent Dual‐Class Service Systems*

This article addresses the optimal staffing problem for a nonpreemptive priority queue with two customer classes and a time‐dependent arrival rate. The problem is related to several important service settings such as call centers and emergency departments where the customers are grouped into two classes of “high priority” and “low priority,” and the services are typically evaluated according to the proportion of customers who are responded to within targeted response times. To date, only approximation methods have been explored to generate staffing requirements for time‐dependent dual‐class services, but we propose a tractable numerical approach to evaluate system behavior and generate safe minimum staffing levels using mixed discrete‐continuous time Markov chains (MDCTMCs). Our approach is delicate in that it accounts for the behavior of the system under a number of different rules that may be imposed on staff if they are busy when due to leave and involves explicitly calculating delay distributions for two customer classes. Ultimately, we embed our methodology in a proposed extension of the Euler method, coined Euler Pri, that can cope with two customer classes, and use it to recommend staffing levels for the Welsh Ambulance Service Trust (WAST).     

超宽带LFM信号数字产生系统的误差分析

对线性调频(LFM)信号的三种主要数字产生方法进行分析,提出一种基于波形存储直读法的超宽带线性调频脉冲压缩信号数字产生系统方案。探讨了系统数字基带中的幅度量化位数和采样频率、正交调制器的非理想性、倍频电路中的误差因素和中频LFM信号有时域畸变时对输出信号性能的影响,为超宽带系统工程实现时参数选择、性能评估和性能优化提供重要的理论依据和经验参考。     

On the effect of the number of quadrature points in a logistic random effects model: an example

Although generalized linear mixed models are recognized to be of major practical importance, it is also known that they can be computationally demanding. The problem is the evaluation of the integral in calculating the marginalized likelihood. The straightforward method is based on the Gauss–Hermite technique, based on Gaussian quadrature points. Another approach is provided by the class of penalized quasi-likelihood methods. It is commonly believed that the Gauss–Hermite method works relatively well in simple situations but fails in more complicated structures. However, we present here a strikingly simple example of a logistic random-intercepts model in the context of a longitudinal clinical trial where the method gives valid results only for a high number of quadrature points ( Q ). As a consequence, this result warns the practitioner to examine routinely the dependence of the results on Q . The adaptive Gaussian quadrature, as implemented in the new SAS procedure NLMIXED, offered the solution to our problem. However, even the adaptive version of Gaussian quadrature needs careful handling to ensure convergence.     

Nonparametric estimation of welfare changes

In this article, I propose a simple nonparametric method of estimating welfare measures with no structural assumptions on the demand system. The method is illustrated for both the single good and the multiple good case, using simulated data. Standard errors of the welfare changes are derived using standard bootstrap techniques. The compensating variation and deadweight loss resulting from a housing price increase is evaluated using Canadian mortgage data. The GAUSS coding, which is very short, is included.     

Even More Direct Calculation of the Variance of a Maximum Penalized-Likelihood Estimator

We discuss here two examples of estimation by numerical maximization of penalized likelihood. We show that, in these examples, it is simpler not to use the EM algorithm for computation of the estimates or their standard errors. We discuss also confidence and credibility intervals based on penalized likelihood and a chi-squared approximate distribution, and compare such intervals with intervals of Wald type.

[Received July 2014. Revised September 2015.]     

Discussion

A number of studies have investigated the numerical reliability of the GAUSS Mathematical and Statistical System by Aptech Inc. Those studies identified several critical accuracy errors for different computational methods. We conducted comprehensive tests of this widely used package on estimation, statistical distributions, and random number generation and found that GAUSS 8.0 still has serious problems, especially with statistical distributions and random number generation.     

Inference about the Ratio of Two Parameters,with Application to Whale Censusing

Inference for the quotient of two parameters estimated separately may be obtained by the delta method. When the distribution of linear transformations involving the numerator and the denominator is available, more exact and elementary methods may be used. Non-Bayesian and Bayesian approaches are developed. The application of the methods to estimating the stock abundance of Northeastern Atlantic minke whales, where the ratio is a raw estimate divided by a measure of observation efficiency, is explained and discussed. The Bayesian approach allows exact inference in quite general situations using only a single, rapidly implemented, one-dimensional numerical integration. A simple analytic approximation is given for the common situation where the joint posterior distribution of the numerator and denominator can be approximated by a normal distribution that gives very little probability to negative values of the denominator. The Bayesian approach also permits the incorporation of model uncertainty (or disagreement) in a natural way, and this was the basis for the conclusions of the International Whaling Commission Scientific Committee at its 1990 meeting.     

Errors-in-variables beta regression models

Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0, 1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is employed. Three estimation methods are presented, namely maximum likelihood, maximum pseudo-likelihood and regression calibration. Monte Carlo simulations are used to evaluate the performance of the proposed estimators and the naïve estimator. Also, a residual analysis for beta regression models with measurement errors is proposed. The results are illustrated in a real data set.     

Saddlepoint Approximations to the Limiting Distribution of the Modified Anderson–Darling Test Statistic

The approximation for the distribution function of test statistic is extremely important in statistics. The standard and higher-order saddlepoint approximations are considered in tails of the limiting distribution for the modified Anderson–Darling test. The saddlepoint approximations are compared with the approximation of Sinclair et al. (1990 Sinclair , C. D. , Spurr , B. D. , Ahmad , M. I. ( 1990 ). Modified Anderson Darling test . Communication Statistics—Theory and Methods 19 : 3677 – 3686 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) for upper tail area. An empirical function is derived to estimate the critical values of a saddlepoint approximation.