Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.
Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.
System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notations, learning just how the Kalman filter works can be a daunting task. With its mathematically rigorous, “no frills” approach to the basic discrete-time Kalman filter, A Kalman Filter Primer builds a thorough understanding of the inner workings and basic concepts of Kalman filter recursions from first principles. Instead of the typical Bayesian perspective, the author develops the topic via least-squares and classical matrix methods using the Cholesky decomposition to distill the essence of the Kalman filter and reveal the motivations behind the choice of the initializing state vector. He supplies pseudo-code algorithms for the various recursions, enabling code development to implement the filter in practice. The book thoroughly studies the development of modern smoothing algorithms and methods for determining initial states, along with a comprehensive development of the “diffuse” Kalman filter. Using a tiered presentation that builds on simple discussions to more complex and thorough treatments, A Kalman Filter Primer is the perfect introduction to quickly and effectively using the Kalman filter in practice.
With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.
CBD is the path to healing—without a high Many of us have become accustomed to quick-fix prescriptions for pain relief that mask symptoms, but there's a safer, more natural solution: cannabidiol (CBD). Pain-Free with CBD gives you a look into the therapeutic benefits of CBD, including how it can decrease inflammation and provide relief for your painful condition—without the side effects. Pain-Free with CBD offers guidance on choosing the best product, safely dosing, and finding the best method of consumption for you. Manage a variety of ailments including arthritis, fibromyalgia, lupus—and even the health of your furry friends. Discover the history of this natural medicine and success stories of others who have regained their quality of life with the help of CBD. Pain-Free with CBD includes: Nature's medicine—Explore an overview of your body's endocannabinoid system (ECS) and how CBD works to heal the body. Quality choice—Learn how to select high-quality products using suggestions on top CBD brands, safety tips, and helpful how-tos for each delivery method. Dosing guidance—Determine the right dosage for your needs and learn about possible interactions between CBD and commonly prescribed medications. Discover how CBD can be the healing solution to your pain—one drop at a time.
The second issue of Black Cat Weekly presents more tales of the mysterious and fantastic—four mystery shorts, a mystery novel, four science fiction stories, and a fantasy novel, by some of the greatest writers of all time. Here are: IT’S A MAD, MAD, MAD, MAD GIRL! by Jeff Cohen [Barb Goffman Presents - mystery short story] THE MYSTERY OF THE TRUST BUILDER, by Frank Lovell Nelson [Serial story - 2 of 12] ALWAYS READ THE FINE PRINT, by Hal Charles [Solve it yourself mystery!] THE TWISTED INN, by Hugh Walpole [mystery short story] FALSE TO ANY MAN, by Leslie Ford [mystery novel] THE TELL, by David Brin [Paul Di Filippo Presents - sci-fi short story] MRS. PIGAFETTA SWIMS WELL, by Reginald Bretnor [sci-fi short story] THIRTY DAYS HATH SEPTEMBER, by Robert F. Young [sci-fi short story] THE ALIEN DIES AT DAWN, by Randall Garrett and Robert Silverberg [sci-fi short story] THE ENCHANTED CRUSADE, by Geoff St. Reynard [fantasy novel]
This is a revised edition by David Herbert Donald of his former professor J. G. Randall’s book The Civil War and Reconstruction, which was originally published in 1937 and had long been regarded as “the standard work in its field”, serving as a useful basic Civil War reference tool for general readers and textbook for college classes. This Second Edition retains many of the original chapters, “such as those treating border-state problems, non-military developments during the war, intellectual tendencies, anti-war efforts, religious and educational movements, and propaganda methods [...] bearing evidence of Mr. Randall’s thoroughgoing exploration of the manuscripts and archives,” whilst it expands considerably on other original chapters, such as those relating to the Confederacy. Still other portions have been entirely recast or rewritten, such as the pre-war period chapters and Reconstruction chapters, reflecting factual updates since Randall’s original publication. A must-read for all Civil War students and scholars.
This practical handbook aims to show planners and managers throughout the financial services industry how to compete successfully by improving the quality, selection, and delivery of services. It presents step-by-step methods for designing and implementing financial service packages that will satisfy customers' needs. It offers practical advice on how to determine customers' wants and how to translate these into an individualized package tailored to their particular needs Business Information Alert In recent years, the U.S. housing market has been characterized by rapid changes in housing prices, quality, and availability. This handbook is a highly readable examination of the various theories that have been advanced to explain the economic behavior of today's housing market. Emphasis is put on developing an understanding of the sophisticated economics underlying the market, thus enabling the reader to carry this knowledge over into a rapidly changing marketplace. The book begins with a brief look at the historical development of U.S. housing markets and government intervention in these markets. The study goes on to develop a conceptual framework that can be used to evaluate the effects of the economic environment and government policy on the housing market. Throughout the book, real-world data is employed to verify and illustrate the major points of the presentation.
The nation's most successful adoption attorney fully explains every aspect of adoption for prospective patents, including: - twelve types of adoption- insiders' strategies for adoption success- how to spot red flags around a risky adoption- adoption myths- lists every child-placing agency in each state- biographies of adoption attorneys- reviews of each state's unique adoption laws and procedures (When is the consent signed by the birth mother? How long does she have to change her mind?)
Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.
System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notation
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.
Parallel processing can be ideally suited for the solving of more complex problems in statistical computing. This book discusses code development in C++ and R, before going beyond to look at the valuable use of these two languages in unison. It covers linear equation solution with regression and linear models motivation, optimization with maximum likelihood and nonlinear least squares motivation, and random number generation. While the text does require a working knowledge of basic concepts in statistics and experience in programming, it does not require knowledge specific to C++ or R.
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