A Conversation with James O. Ramsay

Jim Ramsay was born on September 5, 1942, in Prince George, British Columbia. He pursued undergraduate studies at the University of Alberta, where he completed a BEd in 1964 with a major in English and a minor in mathematics. He then specialized in statistics and psychometry, earning a PhD in psychology from Princeton University in 1966. After holding a temporary lectureship in the Department of Psychology at University College London for one year, he joined the Department of Psychology at McGill University, where he rose through the academic ranks. He was chair of his department from 1986 to 1989 and spent sabbatical leaves in Cambridge, Grenoble, and Toulouse. He was named professor emeritus upon his retirement in 2007. Jim is the author of four influential books and over 100 peer‐reviewed articles in statistical and psychometric journals. He developed much of the statistical theory behind multidimensional scaling and is widely recognized as the founder of functional data analysis. Three of his papers were read to the Royal Statistical Society, and another won The Canadian Journal of Statistics 2000 Best Paper Award. The Statistical Society of Canada (SSC) awarded him a Gold Medal for research in 1998 and an honorary membership in 2012. Jim was president of the Psychometric Society in 1981–82 and president of the SSC in 2002–03. The following conversation took place at Jim's home in Ottawa, Ontario, on March 14 and April 4, 2012.     

Institutional dimensions underlying public trust in information on technological risk

This paper examines the determinants of trust in public information on technological risk in a petrochemical complex located in Tarragona (Spain). Data from focus groups (eight) and a questionnaire survey (N = 400) are drawn together to analyse how two local communities exposed to major chemical hazards perceive information on risk and its sources. Results show how trust relies on two main factors, namely expertise and trustworthiness, that are significantly influenced by a third one, antagonism. Results also illustrate the relevance of the institutional context when understanding how communities give meaning to the available information on risk.     

A Fuzzy clustering approach to the key sectors of the Spanish economy

The search for key sectors in an economy has been and still is one of the more recurrent themes in input–output analysis. When using clustering techniques, sectors can only belong to a group, having a particular performance. But, actually, the same sector could be important from different perspectives at the same time, to a different degree. So, a fuzzy clustering approach is needed. In this work we propose a multidimensional approach to classify the productive sectors of the Spanish input–output table for 1995, based on three groups of variables: those related to their productive integration, others measuring their specific weight in the economy and finally some showing their economic dynamic. We also incorporate into the analysis the technological level, which being a categorical variable presents special methodological problems. All these questions are tackled applying a robust and fuzzy clustering analysis, which gives as a result a classification of sectors illustrating the role that each one plays in the Spanish economy.     

Intermediation for technology diffusion and user innovation in a developing rural economy: a social learning perspective

Technology intermediaries are seen as potent vehicles for addressing perennial problems in transferring technology from university to industry in developed and developing countries. This paper examines what constitutes effective user-end intermediation in a low-technology, developing economy context, which is an under-researched topic. The social learning in technological innovation framework is extended using situated learning theory in a longitudinal instrumental case study of an exemplar technology intermediation programme. The paper documents the role that academic-related research and advisory centres can play as intermediaries in brokering, facilitating and configuring technology, against the backdrop of a group of small-scale pisciculture businesses in a rural area of Colombia. In doing so, it demonstrates how technology intermediation activities can be optimized in the domestication and innofusion of technology amongst end-users. The design components featured in this instrumental case of intermediation can inform policy making and practice relating to technology transfer from university to rural industry. Future research on this subject should consider the intermediation components put forward, as well as the impact of such interventions, in different countries and industrial sectors. Such research would allow for theoretical replication and help improve technology domestication and innofusion in different contexts, especially in less-developed countries.     

Applying free random variables to random matrix analysis of financial data. Part I: The Gaussian case

We apply the concept of free random variables to doubly correlated (Gaussian) Wishart random matrix models, appearing, for example, in a multivariate analysis of financial time series, and displaying both inter-asset cross-covariances and temporal auto-covariances. We give a comprehensive introduction to the rich financial reality behind such models. We explain in an elementary way the main techniques of free random variables calculus, with a view to promoting them in the quantitative finance community. We apply our findings to tackle several financially relevant problems, such as a universe of assets displaying exponentially decaying temporal covariances, or the exponentially weighted moving average, both with an arbitrary structure of cross-covariances.