Dialogue and Impromptu Words

Western philosophy and Chinese wisdom seem incommensurable: one employs dialogue the other impromptu words. It seems as if the very notion of universality is tied to the Western notion of dialogue, and that dialogue between the two cultures necessarily will be on Western terms and put Chinese thought in a subaltern position. I show, on the contrary, that it is possible to develop a non-dialogical notion of universality based on Zhuangzi's impromptu words. I compare Zhuangzi's impromptu words with the function of the word in the later Heidegger, and show that it was on the basis of the universal dimension opened up by the impromptu word that Heidegger was able to encounter Chinese thought.     

Rhetorical Theory and the Critique of National Identity Construction

A striking feature of scholarship on national identity is the relative absence of rhetorical theory, or theory related to the persuasive dimensions of discourse, especially given the fact that rhetorical theorists have been concerned with the manufacturing of public opinion and collective identity for over two millennia. To address this absence, this article discusses how rhetorical theories dealing with narrative theory, the social construction of publics, rhetorical constraints, ideology critique, public memory, political history and post-national identity help both to illuminate and critique emergent patterns of national identification.     

The rise of militant Bretonité

The Breton movement in France grew increasingly militant in the late twentieth century, in part due to the French government. After its official and unofficial repression of Bretons ended in the mid-twentieth century, France never developed a consistent policy to deal with Breton culture. At the same time, the Bretons themselves changed their political outlook. The rise of militant Bretonité resulted from a new belief among the activists that their survival depended on government intervention. This change led to a shift in Breton politics from the right to the left, and an overall change in the movement itself.     

A comparative study on national pride among Iranians who live in Canada,those who applied for immigration to Canada and Tehran residents

This study examines the role of national pride on immigration. The main question is whether national pride in Iranian residents of Canada, those who applied for immigration to Canada and Tehran residents is different. A total of 75 Iranian residents who were living in Canada, 99 Iranians who had applied for immigration to Canada and 98 Iranians who were living in Tehran participated in the study and filled out the International Social Survey Programme (ISSP) questionnaire. Participants in the three groups were generally matched in terms of their demographic characteristics. Results revealed that in all items, except for two, there were significant differences between groups – that is, Iranian residents of Canada had the lowest national pride, while non-immigrant Iranians had the highest national pride. Although the national pride scores of the immigration applicants were between those of the immigrants and non-immigrants, the former group was more similar to the immigrants. Discussion is focused on explaining the reporting role of different components of national pride in forming one's attitude towards immigration. It is concluded that political dissatisfaction, accompanied by a sense of low levels of social welfare, leads to reduce social attachment and brings up the immigration issues. It is also concluded that one of the most effective and urgent strategies to increase national pride in Iran is to put more emphasis and attention on Iran's culture, art and history.     

Testing parameter of the power series distribution of a zero inflated power series model

The Zero Inflated Power Series Distribution (ZIPSD) contains two parameters. The first parameter indicates inflation of zero and the other parameter is that of the Power Series distribution. We provide three asymptotic tests for testing the parameter of Power Series distribution, using an unconditional (standard) likelihood approach, a conditional likelihood approach and a test based on sample mean, respectively. The performance of these three tests has been studied for Zero Inflated Poisson Distribution (ZIPD). Asymptotic Confidence Intervals for the parameter are also provided.     

Feature significance in generalized additive models

This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.     

ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.