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mODa 11 - Advances in Model-Oriented Design and Analysis
  • Language: en
  • Pages: 256

mODa 11 - Advances in Model-Oriented Design and Analysis

  • Type: Book
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  • Published: 2016-06-06
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  • Publisher: Springer

This volume contains pioneering contributions to both the theory and practice of optimal experimental design. Topics include the optimality of designs in linear and nonlinear models, as well as designs for correlated observations and for sequential experimentation. There is an emphasis on applications to medicine, in particular, to the design of clinical trials. Scientists from Europe, the US, Asia, Australia and Africa contributed to this volume of papers from the 11th Workshop on Model Oriented Design and Analysis.

MODA 5 - Advances in Model-Oriented Data Analysis and Experimental Design
  • Language: en
  • Pages: 297

MODA 5 - Advances in Model-Oriented Data Analysis and Experimental Design

This volume contains the majority of the papers presented at the 5th Inter national Workshop on Model-Oriented Data Analysis held in June 1998. This series started in March 1987 with a meeting on the Wartburg, Eisenach (Germany). The next three meetings were in 1990 (St Kyrik monastery, Bulgaria), 1992 (Petrodvorets, StPetersburg, Russia) and 1995 (Spetses, Greece). The main purpose of these workshops was to bring together lead ing scientists from 'Eastern' and 'Western' Europe for the exchange of ideas in theoretical and applied statistics, with special emphasis on experimen tal design. Now that the separation between East and West has become less rigid, this dialogue has, in principle, bec...

Bayesian Learning for Neural Networks
  • Language: en
  • Pages: 194

Bayesian Learning for Neural Networks

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Robustness Against M-dependent Errors in Linear Models
  • Language: en
  • Pages: 8

Robustness Against M-dependent Errors in Linear Models

  • Type: Book
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  • Published: 1993
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  • Publisher: Unknown

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Case Studies in Environmental Statistics
  • Language: en
  • Pages: 207

Case Studies in Environmental Statistics

This book offers a set of case studies exemplifying the broad range of statis tical science used in environmental studies and application. The case studies can be used for graduate courses in environmental statistics, as a resource for courses in statistics using genuine examples to illustrate statistical methodol ogy and theory, and for courses in environmental science. Not only are these studies valuable for teaching about an essential cross-disciplinary activity but they can also be used to spur new research along directions exposed in these examples. The studies reported here resulted from a program of research carried on by the National Institute of Statistical Sciences (NISS) during th...

mODa 10 – Advances in Model-Oriented Design and Analysis
  • Language: en
  • Pages: 254

mODa 10 – Advances in Model-Oriented Design and Analysis

This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent...

mODa 9 – Advances in Model-Oriented Design and Analysis
  • Language: en
  • Pages: 259

mODa 9 – Advances in Model-Oriented Design and Analysis

Statisticians and experimentalists will find the latest trends in optimal experimental design research. Some papers are pioneering contributions, leading to new open research problems. It is a colection of peer reviewed papers.

MODA4 — Advances in Model-Oriented Data Analysis
  • Language: en
  • Pages: 295

MODA4 — Advances in Model-Oriented Data Analysis

This volume is the proceedings of the 4th International Workshop on Model-Oriented Data Analysis. This series of events originated in 1987 at a meeting in Eisenach, that successfully brought together scientists from numerous countries of the 'East ' and 'West'. Now that this distinction is obsolete dialogue has been greatly facilitated, providing opportunities for this dialogue, however, is as vital as ever. The present meeting at Spetses, Greece from 5th to 9th of June 1995 again assembles statisticians from all over the world as this book documents. The hospitality offered by the University of Economics of Athens and the Korgialenios School made it possible to organize this workshop. The e...

Measuring Business Cycles in Economic Time Series
  • Language: en
  • Pages: 198

Measuring Business Cycles in Economic Time Series

This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series forecasts and back-casts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented using artificial and actual data demonstrate the superiority of the alternative strategy.

Multivariate Dispersion, Central Regions, and Depth
  • Language: en
  • Pages: 316

Multivariate Dispersion, Central Regions, and Depth

This book has many applications to stochastic comparison problems in economics and other fields. It covers theory of lift zonoids and demonstrates its usefulness in multivariate analysis, an informal introduction to basic ideas, and a comprehensive investigation into the theory, as well as various applications of the lift zonoid approach and may be separately studied. Readers are assumed to have a firm grounding in probability at the graduate level.