Bücher veröffentlicht von Springer New York

This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression,- gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear,logistic,proportional hazards,andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor:thatis,t-tests,one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2 2k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model "e;held ?xed"e;.

While steroids from the periphery have profound effects on the nervous system, the nervous system also produces its own steroids de novo ("e;neurosteroids"e;). The physiological importance of neurosteroids is beginning to be understood. These steroids potentially have roles in sedative/hypnotic behavior, anxiety, learning, and memory. At the cellular level, neurosteroids affect neuronal excitability, synaptic plasticity and cell proliferation and survival. Early findings hold promise for future strategies to treat specific psychologicalconditions and neurological diseases. This Brief will focus on the current state of understanding of brainderived neurosteroids.

This book explores the challenges and presents best strategies for designing Through-Silicon Vias (TSVs) for 3D integrated circuits. It describes a novel technique to mitigate TSV-induced noise, the GND Plug, which is superior to others adapted from 2-D planar technologies, such as a backside ground plane and traditional substrate contacts. The book also investigates, in the form of a comparative study, the impact of TSV size and granularity, spacing of C4 connectors, off-chip power delivery network, shared and dedicated TSVs, and coaxial TSVs on the quality of power delivery in 3-D ICs. The authors provide detailed best design practices for designing 3-D power delivery networks. Since TSVs occupy silicon real-estate and impact device density, this book provides four iterative algorithms to minimize the number of TSVs in a power delivery network. Unlike other existing methods, these algorithms can be applied in early design stages when only functional block- level behaviors and a floorplan are available. Finally, the authors explore the use of Carbon Nanotubes for power grid design as a futuristic alternative to Copper.

Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.

Viewing and Imaging the Solar System: A Guide for Amateur Astronomers is for those who want to develop their ability to observe and image Solar System objects, including the planets and moons, the Sun, and comets and asteroids. They might be beginners, or they may have already owned and used an astronomical telescope for a year or more. Newcomers are almost always wowed by sights such as the rings of Saturn and the moons of Jupiter, but have little idea how to find these objects for themselves (with the obvious exceptions of the Sun and Moon). They also need guidance about what equipment, besides a telescope, they will need.This book is written by an expert on the Solar System, who has had a lot of experience with outreach programs, which teach others how to make the most of relatively simple and low-cost equipment. That does not mean that this book is not for serious amateurs. On the contrary, it is designed to show amateur astronomers, in a relatively light-hearted-and math-free way-how to become serious.

Network Robustness under Large-Scale Attacks provides the analysis of network robustness under attacks, with a focus on large-scale correlated physical attacks. The book begins with a thorough overview of the latest research and techniques to analyze the network responses to different types of attacks over various network topologies and connection models. It then introduces a new large-scale physical attack model coined as area attack, under which a new network robustness measure is introduced and applied to study the network responses. With this book, readers will learn the necessary tools to evaluate how a complex network responds to random and possibly correlated attacks.

As an introductory textbook on the analysis of variance or a reference for the researcher, this text stresses applications rather than theory, but gives enough theory to enable the reader to apply the methods intelligently rather than mechanically. Comprehensive, and covering the important techniques in the field, including new methods of post hoc testing. The relationships between different research designs are emphasized, and these relationships are exploited to develop general principles which are generalized to the analyses of a large number of seemingly differentdesigns. Primarily for graduate students in any field where statistics are used.

Much of that which is ordinal is modeled as analog. Most computational engines on the other hand are dig- ital. Transforming from analog to digital is straightforward: we simply sample. Regaining the original signal from these samples or assessing the information lost in the sampling process are the fundamental questions addressed by sampling and interpolation theory. This book deals with understanding, generalizing, and extending the cardinal series of Shannon sampling theory. The fundamental form of this series states, remarkably, that a bandlimited signal is uniquely specified by its sufficiently close equally spaced samples. The contents of this book evolved from a set of lecture notes prepared for a graduate survey course on Shannon sampling and interpolation theory. The course was taught at the Department of Electrical Engineering at the University of Washington, Seattle. Each of the seven chapters in this book includes a list of references specific to that chapter. A sequel to this book will contain an extensive bibliography on the subject. The author has also opted to include solutions to selected exercises in the Appendix.

Fundamentals algorithms for SIMD and MIMD hypercubes are developed. These include algorithms for such problems as data broadcasting, data sum, prefix sum, shift, data circulation, data accumulation, sorting, random access reads and writes and data permutation. The fundamental algorithms are then used to obtain efficient hypercube algorithms for matrix multiplication, image processing problems such as convolution, template matching, hough transform, clustering and image processing transformation, and string editing. Most of the algorithms in this book are for hypercubes with the number of processors being a function of problems size. However, for image processing problems, the book also includes algorithms for and MIMD hypercube with a small number of processes. Experimental results on an NCUBE/77 MIMD hypercube are also presented. The book is suitable for use in a one-semester or one-quarter course on hypercube algorithms. For students with no prior exposure to parallel algorithms, it is recommended that one week will be spent on the material in chapter 1, about six weeks on chapter 2 and one week on chapter 3. The remainder of the term can be spent covering topics from the rest of the book.