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Stochastic models. --- Distribution (Probability theory) --- Operations research.

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While the Poisson distribution is a classical statistical model for count data, the distributional model hinges on the constraining property that its mean equal its variance. This text instead introduces the Conway-Maxwell-Poisson distribution and motivates its use in developing flexible statistical methods based on its distributional form. This two-parameter model not only contains the Poisson distribution as a special case but, in its ability to account for data over- or under-dispersion, encompasses both the geometric and Bernoulli distributions. The resulting statistical methods serve in a multitude of ways, from an exploratory data analysis tool, to a flexible modeling impetus for varied statistical methods involving count data. The first comprehensive reference on the subject, this text contains numerous illustrative examples demonstrating R code and output. It is essential reading for academics in statistics and data science, as well as quantitative researchers and data analysts in economics, biostatistics and other applied disciplines.

Distribution (Probability theory) --- R (Computer program language) --- Distribution (Probability theory). --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- GNU-S (Computer program language) --- Domain-specific programming languages

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This book provides a compact and systematic overview of closure properties of heavy-tailed and related distributions, including closure under tail equivalence, convolution, finite mixing, maximum, minimum, convolution power and convolution roots, and product-convolution closure. It includes examples and counterexamples that give an insight into the theory and provides numerous references to technical details and proofs for a deeper study of the subject. The book will serve as a useful reference for graduate students, young researchers, and applied scientists.

Probabilities. --- Distribution (Probability theory). --- Stochastic models. --- Actuarial science. --- Applied Probability. --- Distribution Theory. --- Probability Theory. --- Stochastic Modelling in Statistics. --- Actuarial Mathematics. --- Distribució (Teoria de la probabilitat)

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Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book provides an introduction to the method combined with a unified treatment of GMM statistical theory and a survey of the important developments in the field.

Econometric models. --- Estimation theory. --- Moments method (Statistics). --- Time-series analysis. --- Moments method (Statistics) --- Method of moments (Statistics) --- Mathematical statistics --- Quantitative methods (economics) --- Econometric models --- Time-series analysis --- Estimation theory --- E-books --- Estimating techniques --- Least squares --- Stochastic processes --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- Econometrics --- Mathematical models --- Distribution (Probability theory)

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This book provides robust analysis and synthesis tools for Markovian jump systems in the finite-time domain with specified performances. It explores how these tools can make the systems more applicable to fields such as economic systems, ecological systems and solar thermal central receivers, by limiting system trajectories in the desired bound in a given time interval. Robust Control for Discrete-Time Markovian Jump Systems in the Finite-Time Domain focuses on multiple aspects of finite-time stability and control, including: finite-time H-infinity control; finite-time sliding mode control; finite-time multi-frequency control; finite-time model predictive control; and high-order moment finite-time control for multi-mode systems and also provides many methods and algorithms to solve problems related to Markovian jump systems with simulation examples that illustrate the design procedure and confirm the results of the methods proposed. The thorough discussion of these topics makes the book a useful guide for researchers, industrial engineers and graduate students alike, enabling them systematically to establish the modeling, analysis and synthesis for Markovian jump systems in the finite-time domain.

Control engineering. --- System theory. --- Control theory. --- Stochastic processes. --- Robust statistics. --- Control and Systems Theory. --- Systems Theory, Control . --- Stochastic Systems and Control. --- System Robustness. --- Statistics, Robust --- Distribution (Probability theory) --- Mathematical statistics --- Random processes --- Probabilities --- Dynamics --- Machine theory --- Systems, Theory of --- Systems science --- Science --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Philosophy --- Jump processes. --- Markov processes. --- Time-domain analysis. --- Analysis, Time-domain --- System analysis --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Processes, Jump --- Markov processes