Download On Random Sets Derived From A Subsample For Statistics Based On The Entire Sample full books in PDF, epub, and Kindle. Read online free On Random Sets Derived From A Subsample For Statistics Based On The Entire Sample ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : James Charles Hickman |
| Publisher | : |
| Total Pages | : 64 |
| Release | : 1961 |
| Genre | : Statistics |
| ISBN | : |
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 191 |
| Release | : 2013-09-03 |
| Genre | : Mathematics |
| ISBN | : 0309287812 |
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
| Author | : |
| Publisher | : |
| Total Pages | : 876 |
| Release | : 1962 |
| Genre | : Mathematical statistics |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : 774 |
| Release | : 1961 |
| Genre | : Dissertations, Academic |
| ISBN | : |
| Author | : Institute of Mathematical Statistics |
| Publisher | : |
| Total Pages | : 704 |
| Release | : 1992 |
| Genre | : Mathematical statistics |
| ISBN | : |
| Author | : OECD |
| Publisher | : OECD Publishing |
| Total Pages | : 605 |
| Release | : 2008-09-01 |
| Genre | : |
| ISBN | : 9264055088 |
The OECD Glossary contains a comprehensive set of over 6 700 definitions of key terminology, concepts and commonly used acronyms derived from existing international statistical guidelines and recommendations.
| Author | : Charu C. Aggarwal |
| Publisher | : Springer |
| Total Pages | : 288 |
| Release | : 2017-04-06 |
| Genre | : Computers |
| ISBN | : 3319547658 |
This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.
| Author | : |
| Publisher | : |
| Total Pages | : 16 |
| Release | : 1974 |
| Genre | : Housing |
| ISBN | : |
| Author | : Dick J. Brus |
| Publisher | : CRC Press |
| Total Pages | : 582 |
| Release | : 2022-09-26 |
| Genre | : Mathematics |
| ISBN | : 100060005X |
Scientific research often starts with data collection. However, many researchers pay insufficient attention to this first step in their research. The author, researcher at Wageningen University and Research, often had to conclude that the data collected by fellow researchers were suboptimal, or in some cases even unsuitable for their aim. One reason is that sampling is frequently overlooked in statistics courses. Another reason is the lack of practical textbooks on sampling. Numerous books have been published on the statistical analysis and modelling of data using R, but to date no book has been published in this series on how these data can best be collected. This book fills this gap. Spatial Sampling with R presents an overview of sampling designs for spatial sample survey and monitoring. It shows how to implement the sampling designs and how to estimate (sub)population- and space-time parameters in R. Key features Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping Gives comprehensive overview of model-assisted estimators Covers Bayesian approach to sampling design Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data Data and R code available on github Exercises added making the book suitable as a textbook for students The target group of this book are researchers and practitioners of sample surveys, as well as students in environmental, ecological, agricultural science or any other science in which knowledge about a population of interest is collected through spatial sampling. This book helps to implement proper sampling designs, tailored to their problems at hand, so that valuable data are collected that can be used to answer the research questions.
| Author | : American Statistical Association. Meeting |
| Publisher | : |
| Total Pages | : 292 |
| Release | : 1988 |
| Genre | : Canada |
| ISBN | : |
