The development of statistical theory in Britain, 1865-1925: a historical and sociological perspective

This thesis discusses the development of statistical theory in Britain in the period 1865 to 1925, and attempts to account for this development as an institutional and an intellectual phenomenon. Close connections are shown to have existed between statistical theory as a scientific specialty and eugenics and social Darwinism, in particular in the work of Francis Galton (1822 -1911) and Karl Pearson (1857- 1936). An analysis of eugenics as a social and political movement is presented, and it is argued that eugenics played a major role in facilitating the institutional growth of statistical theory as a field of study. Two scientific controversies involving Karl Pearson and his followers (with William Bateson and the early Mendelians, and with George Udny Yule) are examined, and it is suggested that these controversies might usefully be seen as generated and sustained by divergent social interests. The development of the theory of statistical inference in this period is discussed briefly, and the early pioneering work of W.S. Gosset ('Student') and R.A. Fisher is surveyed.It is concluded that the generation and assessment of scientific innovations by statisticians in this period must be seen as fundamentally affected by social factors having their origins both within science and in the wider society

Space programs summary no. 37-54, volume 3 for the period 1 October to 30 November 1968. Supporting research and advanced development

Spacecraft propulsion, biological environment, guidance and control, electronic components, power supplies, propellants, instrumentation, telecommunication, and mission plannin

Implementation of Bayesian methods in the pharmaceutical industry

This thesis is concerned primarily with the practical implementation of Bayesian methodology within the context of the pharmaceutical industry. The implementation includes the development, where appropriate, of analytic approximations to the posterior distributions of interest and graphical methods for mapping prior assumptions to posterior inference. Two critical areas within pharmaceutical research, critical in the sense of the controversy which they have aroused, have been investigated. First, Bayesian methods for the analysis of two-treatment crossover designs which fell in to disfavour in the late 1970's and early 1980's because of the US Food and Drug Administration's published view that the two-treatment two-period design was not the design of first choice if unequivocal evidence of a treatment effect was required were developed. Each type of design considered and for which methods are developed are illustrated with examples from clinical trials which have already been reported in the medical literature. Second, a Bayesian method is developed whose purpose is to classify test compounds into one of several toxicity classes on the basis of an LD50 estimate. The method is generalised to deal with a non-standard LD50 problem related to the prediction of results from a future LD50 experiment. Both of these applications arose out of a practical consultancy session within the context of a statistics group in the chemical/pharmaceutical industry. As part of the methods required for carrying out these analyses the zeros and weights associated with some non-standard orthogonal polynomial are developed as a result of which a new asymptotic expansion of the Behrens-Fisher density is developed. Further applications of the polynomials orthogonal to t-kernels are developed including problems associated with prediction in clinical trials. A FORTRAN program which has been implemented at a laboratory level within the pharmaceutical toxicology department at CIBA-GEIGY in Switzerland is provided SAS programs for a variety of the analyses developed for the two-treatment crossover designs are provided as are SAS programs for determining the zeros and weights of a number of different classes of orthogonal polynomials

Landslide hazards assessment and uncertainties

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (v. 2, p. 736-750).Landslides are natural phenomena which are difficult to predict because their initiation depends on many factors and on the interaction between these factors. The annual number of causalities caused by landslides is in the thousands, and infrastructural damage is in the billions of dollars. To satisfy the increasingly urgent societal demand for protection against landslides, it is necessary to systematically assess and manage landslide hazard and risk. This can be done using principles of decision making under uncertainty. We develop an advanced combined hydrologic - stability model that is better capable of assessing landslide hazards than current models used in landslide analyses. This model allows one to evaluate landslide hazards deterministically. We use the model to study landslide failure mechanisms, and classify these according to the manner in which a slope gets saturated during rain. We showed that slopes with great depths to bedrock and shallow depths to the water table, tend to fail by saturation from below, resulting in deep seated landslides, and slopes with deep lying water tables tend to fail by saturation from above, resulting in shallow landslides.(cont) Landslide hazards include, by definition, uncertainties which can be expressed probabilistically. Uncertainties arise from parameters and from models. We develop efficient techniques to formally incorporate parameter uncertainties into the combined hydrologic - stability model, and hence into the hazard assessment procedure. We then show that landslide hazards are significantly influenced by the joint probability distribution of the soil strength parameters and the strength submodel(s) used in the stability models, and by the soil characteristic curve submodel(s) used in the hydrologic models. This study leads to a better understanding of landslide mechanisms and to advanced models that assess landslide hazards more accurately than current models. The results of parameter uncertainty investigations show which parameters are most important in landslide analyses, and hence where it is worthwhile to obtain more information. The results of model uncertainty investigations show which models are most important in landslide analyses, and hence where further research needs to be undertaken.by Karim S. Karam.Ph.D

Implementation of Bayesian methods in the pharmaceutical industry

This thesis is concerned primarily with the practical implementation of Bayesian methodology within the context of the pharmaceutical industry. The implementation includes the development, where appropriate, of analytic approximations to the posterior distributions of interest and graphical methods for mapping prior assumptions to posterior inference. Two critical areas within pharmaceutical research, critical in the sense of the controversy which they have aroused, have been investigated. First, Bayesian methods for the analysis of two-treatment crossover designs which fell in to disfavour in the late 1970's and early 1980's because of the US Food and Drug Administration's published view that the two-treatment two-period design was not the design of first choice if unequivocal evidence of a treatment effect was required were developed. Each type of design considered and for which methods are developed are illustrated with examples from clinical trials which have already been reported in the medical literature. Second, a Bayesian method is developed whose purpose is to classify test compounds into one of several toxicity classes on the basis of an LD50 estimate. The method is generalised to deal with a non-standard LD50 problem related to the prediction of results from a future LD50 experiment. Both of these applications arose out of a practical consultancy session within the context of a statistics group in the chemical/pharmaceutical industry. As part of the methods required for carrying out these analyses the zeros and weights associated with some non-standard orthogonal polynomial are developed as a result of which a new asymptotic expansion of the Behrens-Fisher density is developed. Further applications of the polynomials orthogonal to t-kernels are developed including problems associated with prediction in clinical trials. A FORTRAN program which has been implemented at a laboratory level within the pharmaceutical toxicology department at CIBA-GEIGY in Switzerland is provided SAS programs for a variety of the analyses developed for the two-treatment crossover designs are provided as are SAS programs for determining the zeros and weights of a number of different classes of orthogonal polynomials