自由与责任:一种政治哲学的分析

自由和责任是一对孪生概念。但分别从自由出发和从责任出发来处理责任-自由的关系, 我们看到的是两种根本不同的观点。这两种观点体现了两种不同的主体性,即权利在先意识和责任在先意识。分析这两种主体性在社会生活和政治生活中的运行,可以帮助我们从根本上理解当代中西方政治生活的差异性。在权利意义上的自由指的是人在选择中至少面临一种“想要选项”;而责任概念的关键点是个体面向未来、他人和它者。权利在先意识引导着自由主体性,在政治生活上主张把某种普遍的“想要选项”作为人的基本权利,这种政治称为权利政治。责任在先意识所引导的责任主体性,则把政治生活奠基于主体对未来、他人和任务的深入理解,要求每一个人不断培养自己的责任感,并对政治家的责任感提出更高要求,这便是问责政治。这两种由不同主体性所承担的政治体制各有利弊,简单地互相否定不会给我们人类带来任何好处。     

Restricted ridge estimator in generalized linear models: Monte Carlo simulation studies on Poisson and binomial distributed responses

It is known that collinearity among the explanatory variables in generalized linear models (GLMs) inflates the variance of maximum likelihood estimators. To overcome multicollinearity in GLMs, ordinary ridge estimator and restricted estimator were proposed. In this study, a restricted ridge estimator is introduced by unifying the ordinary ridge estimator and the restricted estimator in GLMs and its mean squared error (MSE) properties are discussed. The MSE comparisons are done in the context of first-order approximated estimators. The results are illustrated by a numerical example and two simulation studies are conducted with Poisson and binomial responses.     

Influence and residuals in restricte generalized linear models

A particular influence measure for restricted regression models is reviewed in this paper. We give em- phasis on establishing regularity conditions to apply the proposed influence measure in restricted gen- eralized linear models. The development of conditional residuals is also discussed. In particular, a sim- ulation study was conducted in order to compare the distributions of the proposed residuals for various generalized linear models. Finally, an application is given.     

Characterizing Relationships Between Estimations Under a General Linear Model with Explicit and Implicit Restrictions by Rank of Matrix

In the investigation of the restricted linear model ? _r  = {y, X β | A β = b, σ² Σ}, the parameter constraints A β = b are often handled by transforming the model into certain implicitly restricted model. Any estimation derived from the explicitly and implicitly restricted models on the vector β and its functions should be equivalent, although the expressions of the estimation under the two models may be different. However, people more likely want to directly compare different expressions of estimations and yield a conclusion on their equivalence by using some algebraic operations on expressions of estimations. In this article, we give some results on equivalence of the well-known OLSEs and BLUEs under the explicitly and implicitly restricted linear models by using some expansion formulas for ranks of matrices.     

The Poisson Approximation to the Binomial Distribution

Econometrics textbooks make use of the assumption of a fixed regressor matrix, although it is nearly always unrealistic for economic data. For developing the properties of the general linear model, this is a convenient simplification. But this assumption is retained when examining model specification. This practice has generated results that, if not erroneous, are certainly incomplete and thus deceptive. In particular, the omission of a variable or the imposition of any incorrect restriction may well increase the variance of estimated parameters rather than reduce it, as stated in the textbooks.     

Restricted ridge estimator in the logistic regression model

It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, Schaefer et al. presented a ridge estimator in the logistic regression model. Making use of the ridge estimator, when some linear restrictions are also present, we introduce a restricted ridge estimator in the logistic regression model. Statistical properties of this newly defined estimator will be studied and comparisons are done in the simulation study in the sense of mean squared error criterion. A real-data example and a simulation study are introduced to discuss the performance of this estimator.     

Approximate confidence intervals for a linear combination of binomial proportions: A new variant

We propose a new adjustment for constructing an improved version of the Wald interval for linear combinations of binomial proportions, which addresses the presence of extremal samples. A comparative simulation study was carried out to investigate the performance of this new variant with respect to the exact coverage probability, expected interval length, and mesial and distal noncoverage probabilities. Additionally, we discuss the application of a criterion for interpreting interval location in the case of small samples and/or in situations in which extremal observations exist. The confidence intervals obtained from the new variant performed better for some evaluation measures.     

A nonparametric procedure for testing partially ranked data

In consumer preference studies, it is common to seek a complete ranking of a variety of, say N, alternatives or treatments. Unfortunately, as N increases, it becomes progressively more confusing and undesirable for respondents to rank all N alternatives simultaneously. Moreover, the investigators may only be interested in consumers’ top few choices. Therefore, it is desirable to accommodate the setting where each survey respondent ranks only her/his most preferred k (k?N) alternatives. In this paper, we propose a simple procedure to test the independence of N alternatives and the top-k ranks, such that the value of k can be predetermined before securing a set of partially ranked data or be at the discretion of the investigator in the presence of complete ranking data. The asymptotic distribution of the proposed test under root-n local alternatives is established. We demonstrate our procedure with two real data sets.     

Unmasking the Trend Sought by Jonckheere-Type Tests for Right-Censored Data

Medical and epidemiological studies often involve groups of subjects associated with increasing levels of exposure to a risk factor. Survival of the groups is expected to follow the same order as the level of exposure. Formal tests for this trend fall into the regression framework if one knows what function of exposure to use as a covariate. When unknown, a linear function of exposure level is often used. Jonckheere-type tests for trend have generated continued interest largely because they do not require specification of a covariate. This paper shows that the Jonckheere-type test statistics are special cases of a generalized linear rank statistic with time-dependent covariates which unfortunately depend on the initial group sizes and censoring distributions. Using asymptotic relative efficiency calculations, the Jonckheere tests are compared to standard linear rank tests based on a linear covariate over a spectrum of shapes for the true trend.     

On group divisible response surface (GDRS) designs

In this paper some experimental situations are identified corresponding to which suitable response surface designs do not exist. A class of response surface designs is introduced to cope with these situations. Their analysis with and without blocking and methods of construction is discussed.