为中国人民服务

我来自加拿大安大略省的多伦多市,相信大家对这个地方并不陌生,它是大山的家乡,白求恩医生也在此出生并接受医疗培训。我所在的约克大学就有一个本科生学院是以白求恩医生的名字命名的。白求恩医生的事迹感召着我们．我们每一位获奖外国专家都将永远为我们有机会“为人民服务”,为中国人民服务而心存感激,这是我们无上的荣耀。     

USING DUMMY VARIABLES TO CHECK FOR ROUNDING ERROR IN COMPUTERIZED REGRESSION PROGRAMS*

After a brief review of the role of dummy variables in regression analysis and the current state-of-the art in rounding/truncation error detection in computerized least squares programs, this paper presents a theorem that can be used to detect this type of error whenever an analyst is running a regression program that has one (or more) dummy variables as independent variables.     

REGRESSION METHODOLOGY WITH GROSS OBSERVATION ERRORS IN THE EXPLANATORY VARIABLES***

The robustness of linear programming regression estimators is examined where the disturbance terms are normally distributed and there are observation errors in the explanatory variables. These errors are occasional gross biases between one set of observations and another. The simulation of short series data offers preliminary evidence that when these biases have a non-zero mean, MSAE estimation is more robust than least squares.     

THE EFFECTS OF INTERDISCIPLINARY EDUCATIONAL FORMATS ON CONCEPT RETENTION AND PROBLEM–SOLVING ABILITY*

The purpose of this study was to examine the effects of different interdisciplinary problem-oriented formats on a student's knowledge of and retention of disciplinary concepts and principles, his application of these concepts and principles to disciplinary problems, and his use of a discipline in his analysis of a complex problem. Performances of students enrolled in the experimental sections were compared to those of students enrolled in standard lecture-discussion sections. Regression analysis was used to analyze students' performance in order to control the effects of differences in student backgrounds. It was found that altering the pedagogical format and reward system within the experimental sections had no significant effect upon student performances in the experimental, problem-oriented program. In general, this study indicates that students taught in the standard lecture-discussion format in which grades are determined by examination over course material retain concepts and principles, apply these concepts and principles to disciplinary problems, and integrate disciplinary concepts into their analysis of complex problems as well as, if not better than, students taught in an interdisciplinary, problem-solving format.     

Bandwidth-based nonparametric inference

Nonparametric curve estimation is an extremely common statistical procedure. While its primary purpose has been exploratory, some advances in inference have been made. This paper provides a critical review of inferential tests that make fundamental use of a key element of nonparametric smoothing, the bandwidth, to determine the significance of certain features. A major focus is on two important problems that have been tackled using bandwidth-based inference: testing for the multimodality of a density and testing for the monotonicity of a regression curve. Early research in bandwidth-based inference is surveyed, as well as recent theoretical advances. Possible future directions in bandwidth-based inference are discussed.     

Inference in hybrid Bayesian networks using dynamic discretization

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.     

Interrogating Mixed-Race: A Crisis of Ambiguity?

In many respects, issues surrounding ‘mixed-race’ identities rupture and dislocate much current thinking within post-race and new ethnicities paradigms. This article seeks to critically engage with what is arguably a marginalized and diverse ‘community’. An evaluation of theories emanating from the UK and USA seeks to challenge hybrid degenerative theories, and more recent attempts at explaining how such identities are negotiated in complex social arenas. What emerges is that some theories are underpinned by static and linear behavioural patterns, which appear oversimplified. Key questions are raised in order to further explore whether ‘mixed-race’ identities are genuinely marginalized and remain ambiguous in a range of physical, social, emotional and ideological contexts. Reflecting upon such issues seeks to prompt questions as to whether there can be a more sophisticated and sensitive understanding of the unique experiences (both positive and negative) of ‘mixed-race’ individuals. By doing so, it is hoped that such a topic becomes more central to considerations of how prejudice and discrimination operate across and within a wide variety of settings.