This monograph is multivariate, multi-perspective and multipurpose. We intend to be innovatively integrative through statistical synthesis. Innovation requires capacity to operate in ways that are not ordinary, which means that conventional computations and generic graphics will not meet the needs of an adaptive approach. Flexible formulation and special schematics are essential elements that must be manageable and economical.

Supports the assertion that multiple views of data have a greater prospect of revealing prominent patterns than single views This book provides a portal into the realms of the open source data analysis system called R though exposition by example The authors formulate approaches that are operable in R that support preliminary and progressive prioritization

Autorentext

Dr. Wayne L. Myers earned M.F. and Ph.D. degrees in forest ecology and forest entomology at the University of Michigan. He began his professional career in Canada as a research forest entomologist and biometrician. He then joined the faculty of forestry at Michigan State University specializing in biometrics and remote sensing. The position at Michigan State also encompassed consultancies with the U.S. Forest Service and a work in Brazil. He moved to Penn State University in 1978 in the School of Forest Resources. He is professor of forest biometrics and Director of the Office for Remote Sensing and Spatial Information Resources (ORSSIR) in the Penn State Institutes of Environment. He has thirty-five years of experience in research on development of remote sensing, geographic information systems, and related spatial technologies with applications focusing on natural resources and environment. This extends back to participation as a co-investigator in early investigations of ERTS/LANDSAT as the first spaceborne civilian multispectral sensor. His recent research has focused on dual level progressive segmentation of multispectral images for purposes of compression, integration with geographic information systems and pattern-based change detection. He has developed concepts and computation of echelons of spatial structure in digital surfaces that facilitate extracting major change features from change indicator images. Echelons offer alternatives to thresholding in surface or pseudo-surface rasters. Dome domains provide a further generalization of topological structure in signal surfaces. He has extensive international experience including long-term advisory for the U.S. Agency for International Development in India and research fellowships in Malaysia. He has placed special emphasis on interdisciplinary research and team approach. G.P. Patil: is Distinguished Professor of Mathematical and Environmental Statistics in the Department of Statistics at the Pennsylvania State University, and is a former Visiting Professor of Biostatistics at Harvard University in the Harvard School of Public Health. He has a Ph.D. in Mathematics, D.Sc. in Statistics, one Honorary Degree in Biological Sciences, and another in Letters. GP is a Fellow of American Statistical Association, Fellow of American Association of Advancement of Science, Fellow of Institute of Mathematical Statistics, Elected Member of the International Statistical Institute, Founder Fellow of the National Institute of Ecology and the Society for Medical Statistics in India. GP has been a founder of Statistical Ecology Section of International Association for Ecology and Ecological Society of America, a founder of Statistics and Environment Section of American Statistical Association, and a founder of the International Society for Risk Analysis. He is founding editor-in-chief of the international journal, Environmental and Ecological Statistics and founding director of the Penn State Center for Statistical Ecology and Environmental Statistics. He has published thirty volumes and three hundred research papers. GP has received several distinguished awards which include: Distinguished Statistical Ecologist Award of the International Association for Ecology, Distinguished Achievement Medal for Statistics and the Environment of the American Statistical Association, Distinguished Twentieth Century Service Award for Statistical Ecology and Environmental Statistics of the Ninth Lukacs Symposium, Best Paper Award of the American Fisheries Society, and lately, the Best Paper Award of the American Water Resources Association, among others. Currently, GP is principal investigator of a multi-year NSF grant for surveillance geoinformatics for hotspot detection and prioritization across geographic regions and networks for digital government in the 21st Century.

Klappentext

This monograph is a four-fold featuring of adaptive analysis.

· First is data distillation and comparative coupling whereby the results of one analysis are fed forward into another analysis without necessarily returning directly to the original data matrix, and analytical avenues usually seen as alternatives are pursued in parallel with results being carried forward together as complementary comparatives.

· Second is the flexibility and suitability of the R © statistical software system for engaging in such adaptive and conjunctive statistical strategies. The intention is to provide an extensive entry into the realms of R using exploration by example whereby a demonstrative dataset of manageably moderate size is carried comparatively though the sequence of sections.

· Third is a major mission to introduce innovative methodologies for preliminary and/or partial prioritization that arise from partial order theory. We formulate functions in R that provide for generation and visualization of partial orderings based on combinations of criteria. These methods support etiological exploration for explanations that underlie apparent concurrence or conflict among multiple indicators of suitability or severity.

Fourth is delving more deeply into some multivariate methods such as principal components using the matrix methods available in R . R makes highly compact calls available for several such multivariate methods, but sometimes discernment demands delving into details.

Inhalt
Motivation and Computation.- Part I: Synergistic Scalings, Contingent Clustering and Distance Domains.- Suites of Scalings.- Rotational Rescaling and Disposable Dimensions.- Comparative Clustering for Contingent Collectives.- Distance Domains, Skeletal Structures and Representative Ranks.- Part II: Precedence and Progressive Prioritization.- Ascribed Advantage, Subordination Schematic and ORDIT Ordering.- Precedence Plots, Coordinated Crite4ria and Rank Relations.- Case Comparisons and Precedence Pools.- Distal Data and Indicator Interactions.- Landscape Linkage for Prioritizing Proximate Patches.- Constellations of Criteria.- Severity Setting for Human Health.- Part III: Transformation Techniques and Virtual Variates.- Matrix Methods for Multiple Measures.- Segregating Sets Along Directions of Discrimination.- Index.