Statistical analysis of multivariate computer output

Many scientific investigations rely on computer models for simulating plausible real situations. In trying to describe the complexities of reality, some computer models are themselves very complex and are therefore expensive to run. In response to some of these issues, a recent approach proposes to use statistical models as less computationally demanding surrogates of such complex computer models. The statistical surrogates do not exactly match the computer model output in a new situation, but these have the capability to describe the associated uncertainty. Ideally, the completed statistical model would not require as many computational resources as the original computer model.;Chapter 1 surveys briefly the literature related to the statistical analysis of computer experiments. While most applications implementing the above statistical methodology deal with scalar output, this dissertation suggests methodologies for analyzing multivariate computer output. In particular, Chapter 2 implements a method for the statistical analysis of time series produced by finite difference solvers of differential equations. This statistical model makes use of the underlying code information and, as a result, is second-order non-stationary. The Lotka-Volterra competing species differential system is used as an example to illustrate the methods. It is shown that the statistical model proposed here is more accurate than a statistical model that extends directly the existing scalar methodology to the multivariate case. However, the method is useful only in cases where the output can be easily saved and manipulated numerically. Chapter 3 suggests a two-stage method for the analysis of multivariate computer output in cases when at least one dimension is large, in particular when the number of temporal points is large. A double-gyre ocean system of partial differential equations is used to illustrate this method. Chapter 4 outlines preliminary work on two additional methodologies concerning the statistical analysis of multivariate computer output

Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling

Nonlinear regression is a useful statistical tool, relating observed data and a nonlinear function of unknown parameters. When the parameter-dependent nonlinear function is computationally intensive, a straightforward regression analysis by maximum likelihood is not feasible. The method presented in this paper proposes to construct a faster running surrogate for such a computationally intensive nonlinear function, and to use it in a related nonlinear statistical model that accounts for the uncertainty associated with this surrogate. A pivotal quantity in the Earth's climate system is the climate sensitivity: the change in global temperature due to doubling of atmospheric $\mathrm{CO}_2$ concentrations. This, along with other climate parameters, are estimated by applying the statistical method developed in this paper, where the computationally intensive nonlinear function is the MIT 2D climate model.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS210 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

PRECLINICAL RESEARCH IN HUMAN DRUG DEVELOPMENT

The paper presents the preclinical research requirements according to the legislation in force and it’s implementing guidelines in European Union (EU), tracking down it’s origins to ICH when appropriate. The development of any medicinal product requires its research in terms of quality, safety and efficacy. The new codification system for ICH guidelines adopted by ICH Steering Committee is presented in detail. The five categories of CHMP guidelines are explained. The non-clinical research represents a critical stage for the transition to studies in humans. The paper supports the researchers in their efforts to assess the needs and the timelines in the preclinical research fiel

Statistical analysis of multivariate computer output

Many scientific investigations rely on computer models for simulating plausible real situations. In trying to describe the complexities of reality, some computer models are themselves very complex and are therefore expensive to run. In response to some of these issues, a recent approach proposes to use statistical models as less computationally demanding surrogates of such complex computer models. The statistical surrogates do not exactly match the computer model output in a new situation, but these have the capability to describe the associated uncertainty. Ideally, the completed statistical model would not require as many computational resources as the original computer model.;Chapter 1 surveys briefly the literature related to the statistical analysis of computer experiments. While most applications implementing the above statistical methodology deal with scalar output, this dissertation suggests methodologies for analyzing multivariate computer output. In particular, Chapter 2 implements a method for the statistical analysis of time series produced by finite difference solvers of differential equations. This statistical model makes use of the underlying code information and, as a result, is second-order non-stationary. The Lotka-Volterra competing species differential system is used as an example to illustrate the methods. It is shown that the statistical model proposed here is more accurate than a statistical model that extends directly the existing scalar methodology to the multivariate case. However, the method is useful only in cases where the output can be easily saved and manipulated numerically. Chapter 3 suggests a two-stage method for the analysis of multivariate computer output in cases when at least one dimension is large, in particular when the number of temporal points is large. A double-gyre ocean system of partial differential equations is used to illustrate this method. Chapter 4 outlines preliminary work on two additional methodologies concerning the statistical analysis of multivariate computer output.</p

Inverse Sturm-Liouville problems using multiple spectra

An eigenvalue problem for a Sturm-Liouville differential operator containing a parameter function and being studied on a given domain is a model for the infinitesimal, vertical vibration of a string of negligible mass, with the ends subject to various constraints. The parameter function of the Sturm-Liouville operator encodes information about the string (its density), and the eigenvalues of the same operator are the squares of the natural frequencies of oscillation of the string. In an inverse Sturm-Liouville problem one has knowledge about the spectral data of the operator and tries to recover the parameter function of the same operator. This thesis deals with the recovery of the parameter function of a Sturm-Liouville operator from knowledge of three sets of eigenvalues. The recovery is achieved theoretically and numerically in two different situations: (a) when the three sets correspond respectively to the vibration of the whole string fixed only at the end points, and the vibrations of each individual piece obtained by fixing the string at an interior node; (b) when the three sets correspond respectively to the vibration of the whole string fixed only at the end points, and the vibrations of each individual piece obtained by attaching the string at an interior node to a spring with a known stiffness constant. Situations when existence or uniqueness of the parameter function is lost are also presented.</p

Level design based on sound analysis

Can a song bring us useful information for the creation of a video game