Author: Dimitris N. Politis
Publisher: Springer Science & Business Media
ISBN: 1461215544
Category : Mathematics
Languages : en
Pages : 359
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Book Description
Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. This book sets out to lay some of the foundations for subsampling methodology and related methods.
Author: Nereo Markulic
Publisher: Springer
ISBN: 3030109585
Category : Technology & Engineering
Languages : en
Pages : 156
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Book Description
This book explains concepts behind fractional subsampling-based frequency synthesis that is re-shaping today’s art in the field of low-noise LO generation. It covers advanced material, giving clear guidance for development of background-calibrated environments capable of spur-free synthesis and wideband phase modulation. It further expands the concepts into the field of subsampling polar transmission, where the newly developed architecture enables unprecedented spectral efficiency levels, unquestionably required by the upcoming generation of wireless standards.
Author: Vincent G. Iannacchione
Publisher: RTI Press
ISBN:
Category : True Crime
Languages : en
Pages : 12
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Book Description
The decision to select a subsample of eligible members of a sampled household is influenced by a number of factors including burden on the household, data quality, cost, and the sampling variance of survey estimates. Design effects quantify the influence of a complex sampling design on the variance of survey estimates. Selecting a subsample of eligible persons within a sampled household can have counteracting impacts on design effects. On one hand, subsampling increases the design effects attributable to unequal weighting. On the other hand, subsampling could reduce the design effects attributable to clustering because the potential intra-household correlation among respondents in the same household may be reduced or eliminated. If the reduction in correlation is greater than the increase caused by unequal weighting, subsampling can achieve the same sampling variance as selecting all eligible household members, with less cost and burden. We present the results of a simulation study that evaluates the design effects associated with subsampling household members on personal victimization rates based on the 2008 National Crime Victimization Survey, which selected all persons 12 and older in a sampled household.
Author: Carsten Barth
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 122
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Book Description
Research in RFIC design has recently shifted towards direct conversion and subsampling architectures as an alternative to the conventional super-heterodyne architectures. Bandpass sampling architectures, also called subsampling architectures, exhibit several advantages over super-heterodyne architectures, notably, the complexity of subsampling architectures is significantly lower since no phase-locked loop is required. A direct consequence is that downconversion from RF to IF can be achieved with significant power savings as compared to the super-heterodyne architecture. Another significant benefit of such architectures is the capability for processing multiple signals in parallel. The ability to simultaneously handle multiple carriers makes subsampling architectures particularly well suited for GNSS applications, as downconversion of multiple frequency bands is required in GNSS environments. With the advent of the new civilian GPS signals, L2C and L5, and the onset of the new Galileo signal, a receiver that can process multiple signals without adding complexity, is highly desired. An integrated proof-of-concept subsampling GPS receiver front-end in 130 nm BiCMOS is presented in this dissertation. The receiver achieves a noise figure of less than 3.8 dB, the lowest ever recorded noise figure of a subsampling based receiver.
Author:
Publisher:
ISBN:
Category : Fisheries
Languages : en
Pages : 220
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Book Description
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319547658
Category : Computers
Languages : en
Pages : 288
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Book Description
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: Atul Puri
Publisher: CRC Press
ISBN: 9780203908440
Category : Technology & Engineering
Languages : en
Pages : 662
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Book Description
Describes ITU H H.323 and H.324, H.263, ITU-T video, and MPEG-4 standards, systems, and coding; IP and ATM networks; multimedia search and retrieval; image retrieval in digital laboratories; and the status and direction of MPEG-7.
Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 117
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Book Description
XGBoost is the dominant technique for predictive modeling on regular data. The gradient boosting algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine learning competitors in the world recommend using XGBoost. In this Ebook, learn exactly how to get started and bring XGBoost to your own machine learning projects.
Author: Julio C. Rodríguez-Quiñonez
Publisher: Springer Nature
ISBN: 3031595319
Category :
Languages : en
Pages : 455
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Book Description
Author: Brandon M. Greenwell
Publisher: CRC Press
ISBN: 1000595331
Category : Business & Economics
Languages : en
Pages : 441
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Book Description
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.
