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Estimation of Stochastic Processes with Missing Observations

values of stationary stochastic processes. Estimates are based on observations of the processes with additive stationary noise process. The aim;

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Estimates of Stochastic Processes with Stationary Increments and Cointegrated Sequences

processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It;

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Theory and Statistical Applications of Stochastic Processes

This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It;

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Simulation and Inference for Stochastic Processes with Yuima: A Comprehensive R Framework for Sdes and Other Stochastic Processes

supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic;

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Optimal Statistical Inference in Financial Engineering

Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering;

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Parameter Estimation in Stochastic Volatility Models

well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Statistical Inference for Diffusion Type Processes

refinement of the model are the key steps or building blocks involved in any rational decision making process. Stochastic processes are widely used;

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Statistical Methods for Stochastic Differential Equations

. The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and;

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Parameter Estimation in Fractional Diffusion Models

This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years;

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Stationary Stochastic Processes for Scientists and Engineers

Stochastic processes are indispensable tools for development and research in signal and image processing, automatic control, oceanography;

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Probability, Random Variables and Stochastic Processes

mathematical orientation and comprehensive coverage. The book classifies topics in probability, random variables, and stochastic processes very;

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Stochastic Processes

Markov processes with richly varied queuing applications. The theory and applications of inference, hypothesis testing, estimation, random walks;

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Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems

solving the L2-gain and state-feedback control problems of stochastic switched systems with dwell-time. Advanced Topics in Control and Estimation;

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Nonlinear Stochastic Control and Filtering with Engineering-Oriented Complexities

nonlinear time-delay systems with missing measurement and stochastic disturbances Uses gain-scheduling techniques to tackle the probability;

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Nonlinear Stochastic Control and Filtering With Engineering-Oriented Complexities

functional differential equations Studies the filtering problem for a class of discrete-time stochastic nonlinear time-delay systems with missing;

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Linear Stochastic Systems

of stochastic linear systems, together with their interrelationships. The author first covers the foundations of linear stochastic systems and then;

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Mathematical Statistics and Stochastic Processes

second case is very important for today s practitioners. Mathematical Statistics and Stochastic Processes is based on decision theory and;

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Stochastic Processes

A 'stochastic' process is a 'random' or 'conjectural' process, and this book is concerned with applied probability and statistics. Whilst;

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Applied Stochastic Differential Equations

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical;

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Theory Of Preliminary Test And Stein-Type Estimation With Applications

Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods;

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Deep Learning and Missing Data in Engineering Systems

in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate;

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Levy Matters IV

. These days, statistics for stochastic processes is a lively topic, driven by the needs of various fields of application, such as finance, the;

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Spectral Analysis of Signals

-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from;

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Stochastic Processes: Modeling and Simulation

This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme;

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Stochastic Processes

This book contains examples of Stochastic Processes. It shows how to work with them. Specifically, it shows how to calculate the Mean;

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