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This book is a collection of 19 articles which reflect the courses given at the Collège de France/Summer school "Reconstruction d'images − Applications astrophysiques" held in Nice and Fréjus, France, from June 18 to 22, 2012. The articles presented in this volume address emerging concepts and methods that are useful in the complex process of improving our knowledge of the celestial objects, including Earth. The book contains three parts. The first part is titled "Physical bases and new challenges in high resolution imaging". This part draws a picture of some of the high angular resolution instruments of the near to far future, and of the issues to overcome to make this picture real. It deals with hypertelescopes, optical interferometry, adaptive optics, wavefront coding, and with polychromatic astrophysical models. The point of view of the articles of the second part, titled "Physical models and data processing" embraces not only the description of data using physical modeling, but also the resulting data processing in radio and optical interferometry, including hypertelescopes. The third part is titled "Statistical models in signal and image processing". These contributions cover past and recent developments in multiresolution analysis, Bayesian modeling, sparsity, convex optimization and hyperspectral data. While reading, the alert reader will notice that the successful realization of future observation technologies and the best extraction of the astrophysical information encapsulated in their data involve the joint expertise of several research communities. The various articles collected in this book may contribute to such a synergy.

SCIENCE / Astronomy. --- Images --- Applications astrophysiques --- Statistical models --- Optical models

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Large deviations

Probability theory --- Large deviations. --- Limit theorems (Probability theory) --- 519.21 --- 519.22 --- Large deviations --- Deviations, Large --- Statistics --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- 519.21 Probability theory. Stochastic processes --- Probability theory. Stochastic processes --- Probabilities

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Bioinformatics --- Mathematics. --- Life sciences. --- Bioinformatics. --- Applied mathematics. --- Engineering mathematics. --- Life Sciences. --- Applications of Mathematics. --- Engineering --- Engineering analysis --- Mathematical analysis --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Biosciences --- Sciences, Life --- Science --- Mathematics --- Data processing --- 519.22 --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- Biomathematics. Biometry. Biostatistics --- Stochastic processes --- Bio-informatique --- Mathématiques --- EPUB-LIV-FT LIVBIOLO SPRINGER-B --- Math

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In 'Statistiek in woorden' krijg je op een gebruiksvriendelijke manier, dus zonder formules of technische details, uitgelegd wat de meest voorkomende statistische termen en technieken inhouden (Bron: covertekst).

Statistiek. --- statistiek --- Statistical science --- Mathematical statistics --- 311 --- 519.22 --- #SBIB:303H510 --- Statistiques Statistiek --- #KVHA:Methodologie --- #KVHA:Onderzoeksmethodologie --- #KVHA:Statistiek --- Statistiek --- maatschappijwetenschappen, methoden --- woordenboek --- 517.2 --- S20090832.JPG --- statistieken --- statistiekwetenschap --- 300.6 --- 301.2 --- onderzoeksmethoden --- statistische methoden --- woordenboeken --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- 311 Statistische methoden --- Statistische methoden --- Methoden sociale wetenschappen: statistische technieken, algemeen --- 31 --- Sociaal-wetenschappelijk onderzoek --- sociale en economische statistiek, theorie en methodiek --- statistiek (economische statistiek) --- Mathematische statistiek --- Wiskunde --- Statistiek (theorie)

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This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001–2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004–2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005–2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004. .

Longitudinal method --- Multivariate analysis --- Academic collection --- 519.22 --- 519.23 --- 57.087.1 --- 681.3*G16 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Longitudinal research --- Longitudinal studies --- Methodology --- Research --- Social sciences --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Biometry. Statistical study and treatment of biological data --- 519.23 Statistical analysis. Inference methods --- Statistical analysis. Inference methods --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- #SBIB:303H520 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Longitudinal method. --- Multivariate analysis. --- Analyse multivariée --- Méthode longitudinale --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Distribution (Probability theory. --- Mathematical statistics. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities. --- Statistics . --- Probability --- Combinations --- Chance --- Least squares --- Risk