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Surveys general results on multiple-access channels, and gives an overview of the problems of CDMA solutions. This work includes chapters devoted to the information-theoretical aspects of multiple-access communication. It discusses multiple-access techniques and covers coding techniques.

Multiple access protocols (Computer network protocols) --- Computer network protocols. --- Protocols, Computer network --- Computer networks --- MAP (Multiple access protocols) --- Multi-access protocols (Computer network protocols) --- Multiaccess protocols (Computer network protocols) --- Computer network protocols --- Multiplexing

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The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a procedure which can be interpreted as ?tting a linear relationship to contaminated observed data. Such ?tting of a line through a cloud of points is the classical linear regression problem. A solution of this problem is provided by the famous principle of least squares, which was discovered independently by A. M. Legendre and C. F. Gauss and published in 1805 and 1809, respectively. The principle of least squares can also be applied to construct nonparametric regression estimates, where one does not restrict the class of possible relationships, and will be one of the approaches studied in this book. Linear regression analysis, based on the concept of a regression function, was introduced by F. Galton in 1889, while a probabilistic approach in the context of multivariate normal distributions was already given by A. B- vais in 1846. The ?rst nonparametric regression estimate of local averaging type was proposed by J. W. Tukey in 1947. The partitioning regression - timate he introduced, by analogy to the classical partitioning (histogram) density estimate, can be regarded as a special least squares estimate.

Regression Analysis --- Nonparametric statistics --- Distribution (Probability theory) --- Distribution (Probability theory). --- Nonparametric statistics. --- Regression analysis. --- Stochastic processes --- Mathematical statistics --- Analyse de régression --- Statistique non-paramétrique --- Distribution (Théorie des probabilités) --- EPUB-LIV-FT SPRINGER-B --- Statistics. --- Statistical Theory and Methods. --- Mathematical statistics. --- Statistics . --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric

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Mathematical statistics --- Estimation theory --- Nonparametric statistics --- Time-series analysis --- Théorie de l'estimation --- Statistique non-paramétrique --- Série chronologique --- AA / International- internationaal --- 303.6 --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- Estimating techniques --- Least squares --- Stochastic processes --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Raming : theorie (wiskundige statistiek). Bayesian analysis and inference. --- Estimation theory. --- Nonparametric statistics. --- Time-series analysis. --- Théorie de l'estimation --- Statistique non-paramétrique --- Série chronologique --- Raming : theorie (wiskundige statistiek). Bayesian analysis and inference

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Human sciences (algemeen) --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- Translation science --- Linguistics --- informatica --- sociale wetenschappen --- vertalen --- linguïstiek --- database management --- KI (kunstmatige intelligentie) --- robots --- AI (artificiële intelligentie)