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General ecology and biosociology --- Environmental protection. Environmental technology --- Distribution (Probability theory). --- Ecological risk assessment --- Pollution --- Methodology. --- Environmental aspects.

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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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Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables. This volume collects 12 outstanding empirical contributions in economics and offers an indispensable introduction to interpretation, implementation, and inference aspects of quantile regression.

Distribution (Probability theory) --- Economics --- Regression analysis. --- Statistical methods. --- Quantitative methods (economics) --- Mathematical statistics --- Econometrics. --- Labor economics. --- Public finance. --- Statistics . --- Labor Economics. --- Public Economics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Cameralistics --- Public finance --- Public finances --- Currency question --- Economics, Mathematical --- Statistics --- Economics - Statistical methods.

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Mathematical statistics --- Social sciences --- Probabilities --- Logits --- Probits --- Statistical methods --- 519.2 --- #SBIB:303H10 --- #SBIB:303H520 --- #PBIB:2003.3 --- -Probabilities --- Biomathematics --- Distribution (Probability theory) --- Transformations (Mathematics) --- Logit transformation --- Logarithms --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Risk --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Probability. Mathematical statistics --- Methoden en technieken: algemene handboeken en reeksen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Logits. --- Probabilities. --- Probits. --- Statistical methods. --- 519.2 Probability. Mathematical statistics --- Social sciences - Statistical methods

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"As an overview of fundamental modelling, stability, convergence, and estimation issues for discrete-event systems, this book will be of interest to researchers and graduate students in applied mathematics, operations research, applied probability, and statistics. This book also will be of interest to practitioners of industrial, computer, transportation, and electrical engineering, because it provides an introduction to a powerful set of tools both for modelling and for simulation-based performance analysis."--BOOK JACKET.

Petri nets. --- Stochastic analysis. --- Stochastic processes --- Mathematics. --- Computer simulation. --- Operations research. --- Management science. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Simulation and Modeling. --- Statistical Theory and Methods. --- Operations Research, Management Science. --- Distribution (Probability theory. --- Mathematical statistics. --- Statistics . --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk