THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Statistics for Spatial Data.

http://ee.sharif.edu/~miap/Files/A%20Survey%20of%20Medical%20Image%20Registration.pdf

A survey of medical image registration.

The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain unique optimal values for their parameters using automatic calibration methods. Unless the best set of parameters associated with a given calibration data set can be found, it is difficult to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. Results are presented that establish clearly the nature of the multiple optima problem for the research CRR model SIXPAR. These results suggest that the CRR model optimization problem is more difficult than had been previously thought and that currently used local search procedures have a very low probability of successfully finding the optimal parameter sets. Next, the performance of three existing global search procedures are evaluated on the model SIXPAR. Finally, a powerful new global optimization procedure is presented, entitled the shuffled complex evolution (SCE-UA) method, which was able to consistently locate the global optimum of the SIXPAR model, and appears to be capable of efficiently and effectively solving the CRR model optimization problem.

Effective and efficient global optimization for conceptual rainfall‐runoff models

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.

/pdf/a-pyramid-approach-to-subpixel-registration-based-on-41xhw0es6x.pdf

A pyramid approach to subpixel registration based on intensity

Dual Control of Linearly Parameterised Models via Prediction of Posterior Densities

The problem of optimal experimental design for response optimization is considered. The optimal point (control)x* of a response surface is to be determined by estimating the response parametersθ from measurements performed at design pointsxi,i=1,...,N. Classical sequential approaches for choosing thexi's are recalled. A loss function related to the issue of response optimization is used to define control-oriented design criteria. The design policies differ depending on whether least-squares or minimum risk estimation is used to estimateθ. Connections between various criteria suggested in the literature are exhibited. Special attention is given to quadratic model responses. Most approaches presented assume that the response is correctly described by a given parametric function over the region of interest. Possible deterministic departures from this function raise the problem of model robustness, and the literature on the subject is briefly surveyed.

Experimental design for estimating the optimum point in a response surface

We focus on motion estimation using a block matching approach and suggest using a minimum-entropy criterion. Many entropy-based estimation procedures exist, such as plug-in estimators based on Parzen windowing. We consider here an alternative that is applicable to data of any dimension and that circumvents the critical issues raised by kernel-based methods. To the best of our knowledge, this criterion has not yet been considered for image processing problems. The inherent robustness property of entropy is expected to provide a robust and efficient estimation of the motion vector of a block of a video sequence. In particular, the minimum-entropy estimator should be robust to occlusions and variations of luminance, for which standard approaches like SSD usually meet their limitations.

Luc Pronzato

Papers

Dual Control of Linearly Parameterised Models via Prediction of Posterior Densities

Experimental design for estimating the optimum point in a response surface

A Minimum-Entropy Procedure for Robust Motion Estimation

A minimax equivalence theorem for optimum bounded design measures

Robust Identification and Control Based on Ellipsoidal Parametric Uncertainty Descriptions