A Conversation with Alan Gelfand

Alan E. Gelfand was born April 17, 1945, in the Bronx, New York. He attended public grade schools and did his undergraduate work at what was then called City College of New York (CCNY, now CUNY), excelling at mathematics. He then surprised and saddened his mother by going all the way across the country to Stanford to graduate school, where he completed his dissertation in 1969 under the direction of Professor Herbert Solomon, making him an academic grandson of Herman Rubin and Harold Hotelling. Alan then accepted a faculty position at the University of Connecticut (UConn) where he was promoted to tenured associate professor in 1975 and to full professor in 1980. A few years later he became interested in decision theory, then empirical Bayes, which eventually led to the publication of Gelfand and Smith [J. Amer. Statist. Assoc. 85 (1990) 398-409], the paper that introduced the Gibbs sampler to most statisticians and revolutionized Bayesian computing. In the mid-1990s, Alan's interests turned strongly to spatial statistics, leading to fundamental contributions in spatially-varying coefficient models, coregionalization, and spatial boundary analysis (wombling). He spent 33 years on the faculty at UConn, retiring in 2002 to become the James B. Duke Professor of Statistics and Decision Sciences at Duke University, serving as chair from 2007-2012. At Duke, he has continued his work in spatial methodology while increasing his impact in the environmental sciences. To date, he has published over 260 papers and 6 books; he has also supervised 36 Ph.D. dissertations and 10 postdocs. This interview was done just prior to a conference of his family, academic descendants, and colleagues to celebrate his 70th birthday and his contributions to statistics which took place on April 19-22, 2015 at Duke University.Comment: Published at http://dx.doi.org/10.1214/15-STS521 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Understanding 'The Essential Fact about Capitalism': markets, competition and creative destruction

This paper examines two ways in which competition works in modern capitalist economies to improve productivity. The first is through incentives: encouraging improvements in technology, organisation and effort on the part of existing establishments and firms. The second is through selection: replacing less-productive with more productive establishments and firms, whether smoothly via the transfer of market shares from less to more productive firms, or roughly through the exit of some firms and the entry of others. We report evidence from the UK suggesting that selection is responsible for a large proportion of aggregate productivity growth in manufacturing, and that much of this is due in turn to selection between plants belonging to multi-plant firms. We also investigate whether the nature of the selection process varies across the business cycle and report evidence suggesting that it is less effective in booms and recessions. Finally, although in principle productivity catch-up by low-income countries ought to be easier than innovation at the frontier, in the absence of a well functioning competitive infrastructure (a predicament that characterises many poor countries), selection may be associated with much more turbulence and a lower rate of productivity growth than in relatively prosperous societies. We report results of a survey of firms in transition economies suggesting that, particularly in the former Soviet states (excluding the Baltic states), poor output and productivity performance has not been due to an unwillingness on the part of firms to change and adapt. On the contrary, there has been a great deal of restructuring, much new entry and large reallocations of output between firms; but such activity has been much more weakly associated with improved performance than we would expect in established market economies

Competition, restructuring and firm performance: evidence of an inverted-U relationship from a cross-country survey of firms in transition economies

This paper examines the importance of competition in the growth anddevelopment of firms. We draw on a survey of 3,300 firms in 25transition countries to shed light on the factors that influencerestructuring by firms and their subsequent performance. These datahave three main advantages over those used in previous work. First,they measure directly the degree of competition perceived by each firmin its principal market rather than attempting to infer this from marketdata as measured by statistical agencies. Second, the fact that transitioncountries have market structures inherited from the past avoids some ofthe endogeneity problems associated with measures of competition inmarket economies. Third, the breadth of cross-country variationprovides a method of dealing with the fact that firm-level measures ofthe external environment will not be independent of the firm?s ownperformance. We find evidence of a robust inverted-U effect ofcompetition on performance that is both statistically and economicallysignificant. This paper examines the importance of competition in the growth anddevelopment of firms. We draw on a survey of 3,300 firms in 25transition countries to shed light on the factors that influencerestructuring by firms and their subsequent performance. These datahave three main advantages over those used in previous work. First,they measure directly the degree of competition perceived by each firmin its principal market rather than attempting to infer this from marketdata as measured by statistical agencies. Second, the fact that transitioncountries have market structures inherited from the past avoids some ofthe endogeneity problems associated with measures of competition inmarket economies. Third, the breadth of cross-country variationprovides a method of dealing with the fact that firm-level measures ofthe external environment will not be independent of the firm?s ownperformance. We find evidence of a robust inverted-U effect ofcompetition on performance that is both statistically and economicallysignificant

Variable-speed Generators with Flux Weakening

A cost-competitive, permanent-magnet 20 kW generator is designed such that the following criteria are satisfied: an (over) load capability of at least 30 kW over the entire speed range of 60-120 rpm, generator weight of about 550 lbs with a maximum radial stator flux density of 0.82 T at low speed, unity power factor operation, acceptably small synchronous reactances and operation without a gear box. To justify this final design four different generator designs are investigated: the first two designs are studied to obtain a speed range from 20 to 200 rpm employing rotor field weakening, and the latter two are investigated to obtain a maximum speed range of 40 to 160 rpm based on field weakening via the stator excitation. The generator reactances and induced voltages are computed using finite element/difference solutions. Generator losses and efficiencies are presented for all four designs at rated temperature of Tr=120C

spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models

Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude-longitude, Easting-Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.

The Hydraulic Dimension of Reconstruction in Louisiana, 1863-1879

Louisiana developed an extensive system of levees throughout the Atchafalaya Basin and along its territorial Mississippi River. This system reached its zenith on the eve of the American Civil War. It went into dramatic decline following the conflict due to the confluence of military activity, protracted irregular warfare, and neglect stemming from labor and capital revolution. These shifts intensified with the 1863 Emancipation Proclamation and finally consolidated after the ratification of Louisiana’s Constitution of 1879. The shift of responsibility for the construction and maintenance of levees during the Reconstruction Era led to many significant changes in the character and function of many of the State’s institutions as it struggled to adapt to the postwar order it confronted

The Hydraulic Dimension of Reconstruction in Louisiana, 1863-1879

Louisiana developed an extensive system of levees throughout the Atchafalaya Basin and along its territorial Mississippi River. This system reached its zenith on the eve of the American Civil War. It went into dramatic decline following the conflict due to the confluence of military activity, protracted irregular warfare, and neglect stemming from labor and capital revolution. These shifts intensified with the 1863 Emancipation Proclamation and finally consolidated after the ratification of Louisiana’s Constitution of 1879. The shift of responsibility for the construction and maintenance of levees during the Reconstruction Era led to many significant changes in the character and function of many of the State’s institutions as it struggled to adapt to the postwar order it confronted