Reduced Rank Regression, Heinz Schmidli

Автор: Petros A. Ioannou; Petar V. Kokotovic
Название: Adaptive Systems with Reduced Models
ISBN: 3540121501 ISBN-13(EAN): 9783540121503
Издательство: Springer
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Цена: 81050.00 T
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Автор: Micka?l D. Chekroun; Honghu Liu; Shouhong Wang
Название: Stochastic Parameterizing Manifolds and Non-Markovian Reduced Equations
ISBN: 3319125192 ISBN-13(EAN): 9783319125190
Издательство: Springer
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Цена: 46570.00 T
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Описание: In this second volume, a general approach is developed to provide approximate parameterizations of the "small" scales by the "large" ones for a broad class of stochastic partial differential equations (SPDEs).

Автор: Liu
Название: Robust Rank-Based and Nonparametric Methods
ISBN: 3319390635 ISBN-13(EAN): 9783319390635
Издательство: Springer
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Цена: 102480.00 T
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Описание: The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with readers and implemented. This book is developed from the International Conference on Robust Rank-Based and Nonparametric Methods, held at Western Michigan University in April 2015.

Автор: Hansen
Название: Rank-Deficient and Discrete Ill-Posed Problems
ISBN: 0898714036 ISBN-13(EAN): 9780898714036
Издательство: Mare Nostrum (Eurospan)
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Цена: 71060.00 T
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Описание: Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, seismology, astronomy, and other areas. Rank-deficient problems involve matrices that are exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of given measurements. Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data. This book describes new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and the efficiency and reliability of the computations.

Автор: Carlos N. Bouza-Herrera
Название: Handling Missing Data in Ranked Set Sampling
ISBN: 3642398987 ISBN-13(EAN): 9783642398988
Издательство: Springer
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Цена: 46570.00 T
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Описание: ГЇВїВЅ The existence of missing observations is a very important aspect to be considered in the application of survey sampling, for example. Traditionally, simple random sampling is used to select samples. RSS models are developed as counterparts of well-known simple random sampling (SRS) models.

Автор: Terras, Audrey
Название: Harmonic analysis on symmetric spaces-higher rank spaces, positive definite matrix space and generalizations
ISBN: 1493934066 ISBN-13(EAN): 9781493934065
Издательство: Springer
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Цена: 55890.00 T
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Описание: This text is an introduction to harmonic analysis on symmetric spaces, focusing on advanced topics such as higher rank spaces, positive definite matrix space and generalizations.

Автор: Douglas E. Critchlow
Название: Metric Methods for Analyzing Partially Ranked Data
ISBN: 0387962883 ISBN-13(EAN): 9780387962887
Издательство: Springer
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Цена: 130430.00 T
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Описание: A full ranking of n items is simply an ordering of all these items, of the form: first choice, second choice, *. The problem thus becomes one of ex- tending metrics on the permutation group to metrics on a coset space of the permutation group.

Автор: Mayer Alvo; Philip L.H. Yu
Название: Statistical Methods for Ranking Data
ISBN: 1493914707 ISBN-13(EAN): 9781493914708
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data.

Автор: Rainer Br?ggemann; Ganapati P. Patil
Название: Ranking and Prioritization for Multi-indicator Systems
ISBN: 1461429684 ISBN-13(EAN): 9781461429685
Издательство: Springer
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Цена: 144410.00 T
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Описание: This book provides axioms of partial order and some basic material, for example consequences of "criss-crossing" of data profiles, the role of aggregations of the indicators and the powerful method of formal concept analysis.

Автор: Arnold Janssen; David M. Mason
Название: Non-Standard Rank Tests
ISBN: 0387974849 ISBN-13(EAN): 9780387974842
Издательство: Springer
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Цена: 111790.00 T
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Описание: This monograph extends the notion of locally most powerful rank tests to non-regular cases. An appendix derives the limit experiments needed to investigate the asymptotic optimality of these "non-standard" rank tests under local alternatives.

Автор: Michael A. Fligner; Joseph S. Verducci
Название: Probability Models and Statistical Analyses for Ranking Data
ISBN: 0387979204 ISBN-13(EAN): 9780387979205
Издательство: Springer
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Цена: 111790.00 T
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Описание: In June of 1990, a conference was held on Probablity Models and Statisti- cal Analyses for Ranking Data, under the joint auspices of the American Mathematical Society, the Institute for Mathematical Statistics, and the Society of Industrial and Applied Mathematicians.

Автор: Mayer Alvo; Philip L.H. Yu
Название: Statistical Methods for Ranking Data
ISBN: 1493947818 ISBN-13(EAN): 9781493947812
Издательство: Springer
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Цена: 79190.00 T
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Описание: This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data.