A Conversation with Ingram Olkin

Ingram Olkin was born on July 23, 1924 in Waterbury, Connecticut. His family moved to New York in 1934 and he graduated from DeWitt Clinton High School in 1941. He served three years in the Air Force during World War II and obtained a B.S. in mathematics at the City College of New York in 1947. After receiving an M.A. in mathematical statistics from Columbia in 1949, he completed his graduate studies in the Department of Statistics at the University of North Carolina in 1951. His dissertation was written under the direction of S. N. Roy and Harold Hotelling. He joined the Department of Mathematics at Michigan State University in 1951 as an Assistant Professor, subsequently being promoted to Professor. In 1960, he took a position as Chair of the Department of Statistics at the University of Minnesota. He moved to Stanford University in 1961 to take a joint position as Professor of Statistics and Professor of Education; he was also Chair of the Department of Statistics from 1973--1976. In 2007, Ingram became Professor Emeritus. Ingram was Editor of the Annals of Mathematical Statistics (1971--1972) and served as the first editor of the Annals of Statistics from 1972--1974. He was a primary force in the founding of the Journal of Educational Statistics, for which he was also Associate Editor during 1977--1985. In 1984, he was President of the Institute of Mathematical Statistics. Among his many professional activities, he has served as Chair of the Committee of Presidents of Statistical Societies (COPSS), Chair of the Committee on Applied and Theoretical Statistics of the National Research Council, Chair of the Management Board of the American Education Research Association, and as Trustee for the National Institute of Statistical Sciences. He has been honored by the American Statistical Association (ASA) with a Wilks Medal (1992) and a Founder's Award (1992). The American Psychological Association gave him a Lifetime Contribution Award (1997) and he was elected to the National Academy of Education in 2005. He received the COPSS Elizabeth L. Scott Award in 1998 and delivered the R. A. Fisher Lecture in 2000. In 2003, the City University of New York gave him a Townsend Harris Medal. An author of 5 books, an editor of 10 books, and an author of more than 200 publications, Ingram has made major contributions to statistics and education. His research has focused on multivariate analysis, majorization and inequalities, distribution theory, and meta-analysis. A volume in celebration of Ingram's 65th birthday contains a brief biography and an interview [Gleser, Perlman, Press and Sampson (1989)]. Ingram was chosen in 1997 to participate in the American Statistical Association Distinguished Statistician Video Series and a videotaped conversation and a lecture (Olkin, 1997) are available from the ASA (1997, DS041, DS042).Comment: Published in at http://dx.doi.org/10.1214/088342307000000122 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Min and max scorings for two-sample ordinal data

Journal of the American Statistical Association, March 1992, Vol. 87, No. 417, Theory and MethodsTo analyze two-sample ordinal data, one must often assign some increasing numerical scores to the ordinal categories. The choice of appropriate scores in these types of analyses is often problematic. This article presents a new approach for reporting the results of such analyses. Using techniques of order-restricted inference, we obtain the minimum and maximum of standard two-sample test statistics over all possible assignments of increasing scores. If the range of the min and max values does not include the critical value for the test statistics, then we can immediately conclude that the result of the analysis remains the same no matter what choice of increasing scores is used. On the other hand, if the range includes a critical value, the choice of scores used in the analysis must be carefully justified. Numerous examples are given to clarify our approach

Happy and healthy: a joint model of health and life satisfaction

Subjective well-being has been proposed as an alternative to preference based values of health benefit for use in economic evaluation. We develop a latent factor model of health and well-being in order to compare reported satisfaction with life, satisfaction with health and SF-6D responses. This approach provides a coherent, integrated statistical framework for assessing differences between these outcomes on the same scale. Using panel data from the British Household Panel Survey we find that SF-6D and satisfaction with health are influenced to a similar degree by changes in latent health and satisfaction with life is less responsive. For the average individual, there are no substantial differences in the relative impacts of physical versus mental health conditions between the three measures. These findings suggest that the differences between experienced and hypothetical values of health and life satisfaction may not lead to substantial differences in the assessment of value from health technologies

Happy and healthy: a joint model of health and life satisfaction

Subjective well-being has been proposed as an alternative to preference based values of health benefit for use in economic evaluation. We develop a latent factor model of health and well-being in order to compare reported satisfaction with life, satisfaction with health and SF-6D responses. This approach provides a coherent, integrated statistical framework for assessing differences between these outcomes on the same scale. Using panel data from the British Household Panel Survey we find that SF-6D and satisfaction with health are influenced to a similar degree by changes in latent health and satisfaction with life is less responsive. For the average individual, there are no substantial differences in the relative impacts of physical versus mental health conditions between the three measures. These findings suggest that the differences between experienced and hypothetical values of health and life satisfaction may not lead to substantial differences in the assessment of value from health technologies

Imaging of Convection Enhanced Delivery of Toxins in Humans

Drug delivery of immunotoxins to brain tumors circumventing the blood brain barrier is a significant challenge. Convection-enhanced delivery (CED) circumvents the blood brain barrier through direct intracerebral application using a hydrostatic pressure gradient to percolate therapeutic compounds throughout the interstitial spaces of infiltrated brain and tumors. The efficacy of CED is determined through the distribution of the therapeutic agent to the targeted region. The vast majority of patients fail to receive a significant amount of coverage of the area at risk for tumor recurrence. Understanding this challenge, it is surprising that so little work has been done to monitor the delivery of therapeutic agents using this novel approach. Here we present a review of imaging in convection enhanced delivery monitoring of toxins in humans, and discuss future challenges in the field

Mixture modeling with applications in schizophrenia research

Finite mixture modeling, together with the EM algorithm, have been widely used in clustering analysis. Under such methods, the unknown group membership is usually treated as missing data. When the "complete data" (log-)likelihood function does not have an explicit solution, the simplicity of the EM algorithm breaks down. Authors, including Rai and Matthews (1993), Lange (1995a) and Titterington (1984), developed modified algorithms therefore. As motivated by research in a large neurobiological project, we propose in this paper a new variant of such modifications and show that it is self-consistent. Moreover, simulations are conducted to demonstrate that the new variant converges faster than its predecessors. Originally published Computational Statistics and Data Analysis, Vol. 53, No. 7, May 200

Young people's experiences of managing Type 1 diabetes at university: a national study of UK university students

Aim: Little is known about the challenges of transitioning from school to university for young people with Type 1 diabetes. In a national survey, we investigated the impact of entering and attending university on diabetes self‐care in students with Type 1 diabetes in all UK universities. Methods: Some 1865 current UK university students aged 18–24 years with Type 1 diabetes, were invited to complete a structured questionnaire. The association between demographic variables and diabetes variables was assessed using logistic regression models. Results: In total, 584 (31%) students from 64 hospitals and 37 university medical practices completed the questionnaire. Some 62% had maintained routine diabetes care with their home team, whereas 32% moved to the university provider. Since starting university, 63% reported harder diabetes management and 44% reported higher HbA1c levels than before university. At university, 52% had frequent hypoglycaemia, 9.6% reported one or more episodes of severe hypoglycaemia and 26% experienced diabetes‐related hospital admissions. Female students and those who changed healthcare provider were approximately twice as likely to report poor glycaemic control, emergency hospital admissions and frequent hypoglycaemia. Females were more likely than males to report stress [odds ratio (OR) 4.78, 95% confidence interval (CI) 3.19–7.16], illness (OR 3.48, 95% CI 2.06–5.87) and weight management issues (OR 3.19, 95% CI 1.99–5.11) as barriers to self‐care. Despite these difficulties, 91% of respondents never or rarely contacted university support services about their diabetes. Conclusion: The study quantifies the high level of risk experienced by students with Type 1 diabetes during the transition to university, in particular, female students and those moving to a new university healthcare provider